feat: reindex multiple DataArrays

When e.g. creating a Dataset from multiple DataArrays that are supposed to share the same grid, but are not exactly aligned (as is often the case with floating point coordinates), we usually end up with undesirable NaNs inserted in the data set.
For instance, consider the following data arrays that are not exactly aligned:

import xarray as xr

da1 = xr.DataArray([[0, 1, 2], [3, 4, 5], [6, 7, 8]], coords=[[0, 1, 2], [0, 1, 2]], dims=['x', 'y']).rename('da1')
da2 = xr.DataArray([[0, 1, 2], [3, 4, 5], [6, 7, 8]], coords=[[1.1, 2.1, 3.1], [1.1, 2.1, 3.1]], dims=['x', 'y']).rename('da2')
da1.plot.imshow()
da2.plot.imshow()

They show gaps when combined in a data set:

ds = xr.Dataset({'da1': da1, 'da2': da2})
ds['da1'].plot.imshow()
ds['da2'].plot.imshow()

I think this is a frequent enough situation that we would like a function to re-align all the data arrays together. There is a reindex_like method, which accepts a tolerance, but calling it successively on every data array, like so:

da1r = da1.reindex_like(da2, method='nearest', tolerance=0.2)
da2r = da2.reindex_like(da1r, method='nearest', tolerance=0.2)

would result in the intersection of the coordinates, rather than their union. What I would like is a function like the following:

import numpy as np
from functools import reduce

def reindex_all(arrays, dims, tolerance):
    coords = {}
    for dim in dims:
        coord = reduce(np.union1d, [array[dim] for array in arrays[1:]], arrays[0][dim])
        diff = coord[:-1] - coord[1:]
        keep = np.abs(diff) > tolerance
        coords[dim] = np.append(coord[:-1][keep], coord[-1])
    reindexed = [array.reindex(coords, method='nearest', tolerance=tolerance) for array in arrays]
    return reindexed

da1r, da2r = reindex_all([da1, da2], ['x', 'y'], 0.2)
dsr = xr.Dataset({'da1': da1r, 'da2': da2r})
dsr['da1'].plot.imshow()
dsr['da2'].plot.imshow()

I have not found something equivalent. If you think this is worth it, I could try and send a PR to implement such a feature.

Start by reviewing DataArray.reindex_like and Dataset construction using the examples in the issue. Determine how a union of coordinates and tolerance should be exposed for multiple arrays, then verify that reindexed arrays retain aligned union coordinates without the unwanted NaNs shown in the example.