The region argument has been validated too strictly as coordinates needed to have a common dimension with the selected dimensions in region. The validation is now restricted to data variables.

• [X] Closes #6069
• [X] Tests added
• [X] User visible changes (including notable bug fixes) are documented in whats-new.rst

What happened: Selecting data with ds.sel(index=given_index, method="nearest", tolerance=tolerance) only works in case for each given_index exists an index that is within the given tolerance, otherwise a `KeyError: "not all values found in index 'index'" occurs.

What you expected to happen: I would expect, that those indices that are not within the tolerance would simply be dropped.

Minimal Complete Verifiable Example:

```python import xarray as xr ds = xr.DataArray([1,2,3,4,5], dims=["lat"], coords={'lat':[10,20,30,50,60]})

working example, all latitudes are within the tolerance

ds.sel(lat=[5,15,40], method="nearest", tolerance=10)

<xarray.DataArray (lat: 3)>

array([1, 2, 4])

Coordinates:

* lat (lat) int64 10 20 50

failing example, latitude 40 is not within the tolerance

ds.sel(lat=[5,15,40], method="nearest", tolerance=5)

KeyError: "not all values found in index 'lat'"

```

I would expect ```

ds.sel(lat=[5,15,40], method="nearest", tolerance=5) <xarray.DataArray (lat: 2)> array([1, 2]) Coordinates: * lat (lat) int64 10 20 ```

Anything else we need to know?:

Environment:

What happened?

Applying the mean operator to an xarray dataset that has been grouped by bins, removes variables containing values of type datetime64.

What did you expect to happen?

I expect that all variables are preserved after applying the mean-operator. For min and max this works as expected. sum is not meaningful, so it makes sense that the variable is dropped in that case as well.

Minimal Complete Verifiable Example

```Python import numpy as np import pandas as pd import xarray as xr

times = pd.date_range('2020-01-01', '2020-02-01', freq='1H') index = np.arange(len(times)) bins = np.arange(0,len(index),5) ds=xr.Dataset({'time':('index', times),'float':('index', np.linspace(0,1,len(index)))},coords={'index':index})

ds.groupby_bins('index', bins).mean() <xarray.Dataset> Dimensions: (index_bins: 148) Coordinates: * index_bins (index_bins) object (0, 5] (5, 10] ... (730, 735] (735, 740] Data variables: float (index_bins) float64 0.004032 0.01075 0.01747 ... 0.9852 0.9919

ds.groupby_bins('index', bins).min() <xarray.Dataset> Dimensions: (index_bins: 148) Coordinates: * index_bins (index_bins) object (0, 5] (5, 10] ... (730, 735] (735, 740] Data variables: time (index_bins) datetime64[ns] 2020-01-01T01:00:00 ... 2020-01-3... float (index_bins) float64 0.001344 0.008065 0.01478 ... 0.9825 0.9892 ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

No response

Anything else we need to know?

No response

Environment