BUG: stacked histogram on datetimes
Hello, I want to create a staked histogram, and I hoped to do it with pandas `hist` method. The data I want to plot is in one column of my DataFrame, while one other column is the factor by which I want to color the bars of the histogram. To obtain the actual dataset on which to use the `hist` method, I tried two of the methods listed here: https://stackoverflow.com/questions/41622054/stacked-histogram-of-grouped-values-in-pandas, whith a consistent result like this:

    v= np.array([['2019-10-15T07:33:50.000000000',                           'NaT'],
                 ['2019-10-18T09:19:58.000000000',                           'NaT'],
                 [                          'NaT', '2020-04-26T16:14:38.000000000'],
                 [                          'NaT', '2020-04-27T04:48:30.000000000']],
                dtype= 'datetime64[ns]')
    df= pd.DataFrame(v)

But when I call the histogram on that dataframe:

    df.plot(kind= 'hist', stacked=True)

it gives me the error `ValueError: view limit minimum -0.05000000000000001 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units`.

If, on the other hand, I use method `hist` directly:

    df.hist(stacked= True)

it gives me the error `ValueError: hist method requires numerical columns, nothing to plot`, even if one column alone can be plotted safely.

`NaT` values shouldn't be the problem here, and I assume that both the codes I posted should work, because they produce regular stacked histograms when the columns are numerical instead of datetime.

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 1.0.3
numpy            : 1.18.1
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 46.1.3.post20200330
Cython           : 0.29.17
pytest           : 5.4.1
hypothesis       : 5.8.3
sphinx           : 3.0.3
blosc            : None
feather          : None
xlsxwriter       : 1.2.8
lxml.etree       : 4.5.0
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.13.0
pandas_datareader: None
bs4              : 4.9.0
bottleneck       : 1.3.2
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.5.0
matplotlib       : 3.1.3
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.4.1
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.16
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.8
numba            : 0.43.1

</details>
