Use masked arrays while preserving int

A great beauty of numpys masked arrays is that it works with any dtype, since it does not use nan. Unfortunately, when I try to put my data into an xarray.Dataset, it converts ints to float, as shown below:

In [137]: x = arange(30, dtype="i1").reshape(3, 10)

In [138]: xr.Dataset({"count": (["x", "y"], ma.masked_where(x%5>3, x))}, coords={"x": range(3), "y":
     ...: range(10)})
Out[138]:
<xarray.Dataset>
Dimensions:  (x: 3, y: 10)
Coordinates:
  * y        (y) int64 0 1 2 3 4 5 6 7 8 9
  * x        (x) int64 0 1 2
Data variables:
    count    (x, y) float64 0.0 1.0 2.0 3.0 nan 5.0 6.0 7.0 8.0 nan 10.0 ...

This happens in the function _maybe_promote.

Such type “promotion” is unaffordable for me; the memory consumption of my multi-gigabyte arrays would explode by a factor 4. Secondly, many of my integer-dtype fields are bit arrays, for which floating point representation is not desirable.

It would greatly benefit xarray if it could use masking while preserving the dtype of input data.

(See also: Stackoverflow question)

Start in xarray/core/common.py at _maybe_promote and reproduce the masked-array example from the issue. Trace where the integer masked array becomes float, then determine how the behavior should preserve the input dtype without losing masking; the example should retain integer data rather than promoting it to float.