API: Categorical.get_values returns DatetimeIndex for datetime categories
```python
In [6]: pd.Categorical(['a', 'a', 'b', 'b']).get_values()
Out[6]: array(['a', 'a', 'b', 'b'], dtype=object)

In [7]: pd.Categorical(pd.DatetimeIndex(['2017', '2017', '2018', '2018'])).get_values()
Out[7]: DatetimeIndex(['2017-01-01', '2017-01-01', '2018-01-01', '2018-01-01'], dtype='datetime64[ns]', freq=None)
```

Meanwhile, `Series[Categorical[datetime]].get_values` returns a NumPy array.

```python
In [8]: pd.Series(pd.Categorical(pd.DatetimeIndex(['2017', '2017', '2018', '2018']))).get_values()
Out[8]:
array(['2017-01-01T00:00:00.000000000', '2017-01-01T00:00:00.000000000',
       '2018-01-01T00:00:00.000000000', '2018-01-01T00:00:00.000000000'],
      dtype='datetime64[ns]')
```

This seemed strange to have the return type depend on the categories' dtype. Going to try to find the original motivation. We could consider deprecating this behavior.