Use DatasetGroupBy.quantile for DatasetGroupBy.median for multiple groups when using dask arrays

Is your feature request related to a problem?

I am grouping data in a Dataset and computing statistics. I wanted to take the median over (two) groups, but I got the following message:

>>> ds.groupby(['x', 'y']).median()
# NotImplementedError: The da.nanmedian function only works along an axis or a subset of axes.  The full algorithm is difficult to do in parallel

while ds.groupby(['x']).median() works without any problem.

I noticed that this issue is because the DataArrays are dask arrays: if they are numpy arrays, there is no problem. In addition, if .median() is replaced by .quantile(0.5), there is no problem either. See below:

import dask.array as da
import numpy as np
import xarray as xr

rng = da.random.default_rng(0)
ds = xr.Dataset(
    {'a': (('x', 'y'), rng.random((10, 10)))},
    coords={'x': np.arange(5).repeat(2), 'y': np.arange(5).repeat(2)}
)

# Raises:
# NotImplementedError: The da.nanmedian function only works along an axis or a subset of axes.  The full algorithm is difficult to do in parallel
try:
    ds.groupby(['x', 'y']).median()
except NotImplementedError as e:
    print(e)

# No problems with the following:
ds.groupby(['x']).median()
ds.groupby(['x', 'y']).quantile(0.5)
ds.compute().groupby(['x', 'y']).median() # Implicit conversion to numpy array

Describe the solution you'd like

A straightforward solution seems to be to use DatasetGroupBy.quantile(0.5) for DatasetGroupBy.median() if the median is to be computed over multiple groups.

Describe alternatives you've considered

No response

Additional context

My xr.show_versions():

Start at the DatasetGroupBy.median and DatasetGroupBy.quantile entry points and reproduce the provided two-group dask-array example. Compare the existing behavior for one group, multiple groups, quantile(0.5), and computed NumPy data. Done means multiple-group median works for dask-backed datasets without the reported NotImplementedError, with regression coverage for the example.