Optimize idxmin, idxmax with dask

[dcherian]

• Closes idxmin / idxmax is not parallel friendly #9425
• Tests added
• User visible changes (including notable bug fixes) are documented in whats-new.rst
• New functions/methods are listed in api.rst

cc @phofl here we need to index a numpy array with a dask array (commonly a much larger array) in a sane manner.

We now preserve chunksizes for

import numpy as np
import xarray as xr

# create some dummy data and chunk
x, y, t = 1000, 1000, 57
rang = np.arange(t*x*y)
da = xr.DataArray(rang.reshape(t, x, y), coords={'time':range(t), 'x': range(x), 'y':range(y)})
da = da.chunk(dict(time=-1, x=256, y=256))
da.idxmin('time')

After

Before