Conversion to pandas for zero-dimensional Data(Set|Array)

What happened:
Conversion to a pandas DataFrame of a zero dimensional DataArray or Dataset fails.

What you expected to happen:
I would expect it to return a trivial DataFrame with one row and the respective coordinate / data set columns.
However, I am not sure if that conflicts with potential other round trips between xarray and pandas - e.g. for one-dimensional 1-sized data arrays.

Minimal Complete Verifiable Example:

da = DataArray([1, 2, 3], dims=("x",), coords=dict(x=[1, 2, 3]))
# I don't know of a way to construct such data array without the isel.
# Essentially, below also works for higher dimensional data arrays and
# results in a zero dimensional data array with all the coordinates of
# the found minimum.
da = da.isel(**da.argmin(dim=("x",)))
ds = Dataset({'a': da})
# fails with ValueError: cannot convert a scalar to a DataFrame
# from xarray/core/dataarray.py", line 2664, in to_dataframe
da.to_dataframe(name="foo")
# Expected: a DataFrame with two columns (x and foo) and one row

# fails with ValueError: no valid index for a 0-dimensional object
# from xarray/core/coordinates.py", line 106, in to_index
ds.to_dataframe()
# Expected: a DataFrame with two columns (x and a) and one row

Anything else we need to know?:
I tested a little bit and got what I want with simply removing the

def to_dataframe(...):
    ...
    if self.ndim == 0:
        raise ValueError("cannot convert a scalar to a DataFrame")

block from dataarray.py and changing

def to_index(self, ordered_dims: Sequence[Hashable] = None) -> pd.Index:
   ...
    if len(ordered_dims) == 0:
       return pd.Index([0])
      #  raise ValueError("no valid index for a 0-dimensional object")

to not raise and instead return a trivial index in coordinates.py.

I that would be considered reasonable behavior I am happy to contribute the respective unit test and changes!

Environment:

Start by reproducing the zero-dimensional DataArray and Dataset examples in the issue, then inspect the to_dataframe implementation in dataarray.py and to_index in coordinates.py. Add unit coverage for both conversions and verify that the resulting DataFrames have one row with the expected coordinate and data columns.