id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
937508115,MDU6SXNzdWU5Mzc1MDgxMTU=,5581,Error slicing CFTimeIndex with Pandas 1.3,161133,closed,0,,,4,2021-07-06T04:28:00Z,2021-07-23T21:53:51Z,2021-07-23T21:53:51Z,CONTRIBUTOR,,,,"<!-- Please include a self-contained copy-pastable example that generates the issue if possible.

Please be concise with code posted. See guidelines below on how to provide a good bug report:

- Craft Minimal Bug Reports: http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports
- Minimal Complete Verifiable Examples: https://stackoverflow.com/help/mcve

Bug reports that follow these guidelines are easier to diagnose, and so are often handled much more quickly.
-->

**What happened**:

Slicing a DataArray with a CFTime time axis gives an error `TypeError: _maybe_cast_slice_bound() missing 1 required positional argument: 'kind'`

**What you expected to happen**:

The slice should return elements 31 to 180

**Minimal Complete Verifiable Example**:

```python
import xarray as xr
import cftime
import numpy as np

units = 'days since 2000-01-01 00:00'
time_365 = cftime.num2date(np.arange(0, 10 * 365), units, '365_day')

da = xr.DataArray(np.arange(time_365.size), coords = [time_365], dims = 'time')
da.sel(time=slice('2000-02','2000-06'))
```

**Anything else we need to know?**:

It appears to be a compatibility issue between Pandas 1.3.0 and Xarray 0.18.2, with Pandas 1.2.5 and Xarray 0.18.2 the slice behaves normally. Possibly there has been an interface change that has broken CFTimeIndex.

Using a pure Pandas time axis works fine
```python
import xarray as xr
import pandas as pd
import numpy as np

time = pd.date_range('20000101','20100101', freq='D')

da = xr.DataArray(np.arange(time.size), coords = [time], dims = 'time')
da.sel(time=slice('2000-02','2000-06'))
```

**Environment**:

<details><summary>Output of <tt>xr.show_versions()</tt></summary>

<!-- Paste the output here xr.show_versions() here -->
INSTALLED VERSIONS
------------------
commit: None
python: 3.9.5 | packaged by conda-forge | (default, Jun 19 2021, 00:32:32) 
[GCC 9.3.0]
python-bits: 64
OS: Linux
OS-release: 4.18.0-305.7.1.el8.nci.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.10.6
libnetcdf: 4.7.4

xarray: 0.18.2
pandas: 1.3.0
numpy: 1.21.0
scipy: 1.7.0
netCDF4: 1.5.6
pydap: installed
h5netcdf: 0.11.0
h5py: 3.3.0
Nio: None
zarr: 2.8.3
cftime: 1.4.1
nc_time_axis: 1.2.0
PseudoNetCDF: None
rasterio: 1.2.6
cfgrib: 0.9.9.0
iris: 2.4.0
bottleneck: 1.3.2
dask: 2021.06.2
distributed: 2021.06.2
matplotlib: 3.4.2
cartopy: 0.19.0.post1
seaborn: 0.11.1
numbagg: None
pint: 0.17
setuptools: 52.0.0.post20210125
pip: 21.1.3
conda: 4.10.3
pytest: 6.2.4
IPython: 7.25.0
sphinx: 4.0.3

</details>
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440988633,MDU6SXNzdWU0NDA5ODg2MzM=,2943,Rolling operations loose chunking with dask and bottleneck,161133,closed,0,,,1,2019-05-07T01:52:05Z,2019-05-07T02:01:13Z,2019-05-07T02:01:13Z,CONTRIBUTOR,,,,"#### Code Sample, a copy-pastable example if possible

A ""Minimal, Complete and Verifiable Example"" will make it much easier for maintainers to help you:
http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports

```python
import bottleneck
import xarray
import dask

data = dask.array.ones((100,), chunks=(10,))
da = xarray.DataArray(data, dims=['time'])

rolled = da.rolling(time=15).mean()

# Expect the 'rolled' dataset to be chunked approximately the same as 'data',
# however there is only one chunk in 'rolled' instead of 10
assert len(rolled.chunks[0]) > 1
```
#### Problem description

Rolling operations loose chunking over the rolled dimension when using dask datasets with bottleneck installed, which is a problem for large datasets where we don't want to load the entire thing.

The issue appears to be caused by `xarray.core.dask_array_ops.dask_rolling_wrapper` calling `dask.array.overlap.overlap` on a DataArray instead of a Dask array. Possibly #2940  is related?

#### Expected Output

Chunks should be preserved through `.rolling().mean()`

#### Output of ``xr.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7 | packaged by conda-forge | (default, Feb 28 2019, 09:07:38) 
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 3.10.0-862.14.4.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_AU.utf8
LANG: C
LOCALE: en_AU.UTF-8
libhdf5: 1.10.4
libnetcdf: 4.6.2

xarray: 0.12.1
pandas: 0.24.2
numpy: 1.16.3
scipy: 1.2.1
netCDF4: 1.5.0.1
pydap: installed
h5netcdf: None
h5py: 2.9.0
Nio: None
zarr: 2.3.1
cftime: 1.0.3.4
nc_time_axis: 1.2.0
PseudonetCDF: None
rasterio: None
cfgrib: None
iris: 2.2.0
bottleneck: 1.2.1
dask: 1.2.0
distributed: 1.27.1
matplotlib: 3.0.3
cartopy: 0.17.0
seaborn: 0.9.0
setuptools: 41.0.1
pip: 19.1
conda: None
pytest: 4.4.1
IPython: 7.5.0
sphinx: None
</details>
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