Assigning nan to int-dtype array converts nan to int

(I am almost sure this already exists as an issue, but I can't find the original)

What happened:

When assigning nan to a integer-dtype array, the nan gets incorrectly inverted to int.

What you expected to happen:

I expect to get a ValueError, like I get for pure numpy arrays.

Minimal Complete Verifiable Example:

import xarray
import numpy
da = xarray.DataArray(numpy.array([0,1], dtype="u8"))
da[0] = numpy.nan
print(da)

Gives:

<xarray.DataArray (dim_0: 2)>
array([9223372036854775808,                   1], dtype=uint64)
Dimensions without coordinates: dim_0

Anything else we need to know?:

In numpy the equivalent code raises ValueError

This is related but different from #2945. In #2945, xarray behaves the same as numpy. In #4612, xarray behaves differently from numpy.

Environment:

Start by running the minimal DataArray example and the equivalent NumPy assignment to confirm the differing behavior. Trace the xarray path used by DataArray integer indexing assignment, then add regression coverage for assigning numpy.nan to a uint8 array. Done means xarray raises ValueError instead of converting nan to an integer.