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
1035607476,I_kwDOAMm_X849uh20,5897,ds.mean bugs with cftime objects,20629530,open,0,,,1,2021-10-25T21:55:12Z,2021-10-27T14:51:07Z,,CONTRIBUTOR,,,,"<!-- Please include a self-contained copy-pastable example that generates the issue if possible.

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**What happened**:
Given a dataset that has a variable with cftime objects along dimension A, averaging (`mean`) leads to buggy behaviour:

1. Averaging over 'A' drops the variable instead of averaging it.
2. Averaging over any other dimension will fail if that variable is on the dask backend.

**What you expected to happen**:

1. I expected the average to fail in the case of a dask-backed cftime variable, given that this code exists: https://github.com/pydata/xarray/blob/fdabf3bea5c750939a4a2ae60f80ed34a6aebd58/xarray/core/duck_array_ops.py#L562-L572

And I expected the average to work (not drop the var) in the case of the numpy backend.

2. I expected the fact that dask is used to be irrelevant to the result. I expected the mean to conserve the cftime variable as-is since it doesn't include the averaged dimension.

**Minimal Complete Verifiable Example**:

```python
# Put your MCVE code here
import xarray as xr

ds = xr.Dataset({
    'var1': (('time',), xr.cftime_range('2021-10-31', periods=10, freq='D')),
    'var2': (('x',), list(range(10)))
 })
# var1 contains cftime objects
# var2 contains integers
# They do not share dims

ds.mean('time')  # var1 has disappeared instead of being averaged

ds.mean('x') # Everything ok

dsc = ds.chunk({})

dsc.mean('time') # var1 has disappeared. I would expected this line to fail.

dsc.mean('x') # Raises NotImplementedError. I would expect this line to run flawlessly.
```

**Anything else we need to know?**:
A culprit is #5393, but maybe the bug is older? I think the change introduced there causes the issue (2) above.

In `duck_array_ops.py` the mean operation is declared `numeric_only`, which is kinda incoherent with the implementation allowing means of datetime objects. This setting causes my (1) above. 

**Environment**:

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

INSTALLED VERSIONS
------------------
commit: fdabf3bea5c750939a4a2ae60f80ed34a6aebd58
python: 3.9.7 | packaged by conda-forge | (default, Sep 29 2021, 19:20:46) 
[GCC 9.4.0]
python-bits: 64
OS: Linux
OS-release: 5.14.12-arch1-1
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: fr_CA.utf8
LOCALE: ('fr_CA', 'UTF-8')
libhdf5: 1.12.1
libnetcdf: 4.8.1

xarray: 0.19.1.dev89+gfdabf3be
pandas: 1.3.4
numpy: 1.21.3
scipy: 1.7.1
netCDF4: 1.5.7
pydap: installed
h5netcdf: 0.11.0
h5py: 3.4.0
Nio: None
zarr: 2.10.1
cftime: 1.5.1
nc_time_axis: 1.4.0
PseudoNetCDF: installed
rasterio: 1.2.10
cfgrib: 0.9.9.1
iris: 3.1.0
bottleneck: 1.3.2
dask: 2021.10.0
distributed: 2021.10.0
matplotlib: 3.4.3
cartopy: 0.20.1
seaborn: 0.11.2
numbagg: 0.2.1
fsspec: 2021.10.1
cupy: None
pint: 0.17
sparse: 0.13.0
setuptools: 58.2.0
pip: 21.3.1
conda: None
pytest: 6.2.5
IPython: 7.28.0
sphinx: None

</details>
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