Unexpected NaNs in broadcast

What happened?

When running the broadcast in the sample code, I end up with nan in the output when there are not any in the original source array. While I know the construction is really odd (this came from user-submitted code), I'm shocked that it resulted in nans the resulting broadcasted data and honestly assumed MetPy's code was doing something dumb for quite awhile. I would have expected (regardless of the nature of the coordinates) that the result for broad_a be [[1, 2], [1, 2]].

What did you expect to happen?

No response

Minimal Complete Verifiable Example

levs = np.array([100000, 85000])
a = xr.Dataset({'a': (('lev',), [1, 2])}, coords={'lev': levs}).to_array()
b = xr.Dataset({'b': (('lev',), [3, 4])}, coords={'lev': levs}).to_array()

broad_a, broad_b = xr.broadcast(a, b)
print(broad_a)

MVCE confirmation

• Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• Complete example — the example is self-contained, including all data and the text of any traceback.
• Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

<xarray.DataArray (variable: 2, lev: 2)>
array([[ 1.,  2.],
       [nan, nan]])
Coordinates:
  * lev       (lev) int64 100000 85000
  * variable  (variable) object 'a' 'b'

Anything else we need to know?

No response

Environment

Start by running the provided Python MVCE and inspect the xarray.broadcast entry point and its handling of the two DataArrays. The work is done when the example produces broadcast_a values [[1, 2], [1, 2]] without introducing NaNs, with coverage for this case.