BUG: min/max of empty datetime dataframe raises
- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

#### Code Sample, a copy-pastable example

```python
import pandas as pd
df = pd.DataFrame(dict(x=pd.to_datetime([])))
df.max()
```

```python-traceback
Traceback (most recent call last):
  File "<ipython-input-17-be9940feb663>", line 1, in <module>
    df.max()
  File "pandas/core/generic.py", line 11215, in stat_func
    f, name, axis=axis, skipna=skipna, numeric_only=numeric_only
  File "pandas/core/frame.py", line 7907, in _reduce
    result = f(values)
  File "pandas/core/frame.py", line 7865, in f
    return op(x, axis=axis, skipna=skipna, **kwds)
  File "pandas/core/nanops.py", line 109, in f
    return _na_for_min_count(values, axis)
  File "pandas/core/nanops.py", line 392, in _na_for_min_count
    result.fill(fill_value)
ValueError: cannot convert float NaN to integer
```

#### Problem description

When taking the min/max of an empty datetime dataframe, a ValueError is raised. This is surprising, and inconsistent with the case of an empty datetime series, where min/max return NaT.

#### Expected Output

```python
x   NaT
dtype: datetime64[ns]
```

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.7.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.20.11-100.fc28.x86_64
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_GB.UTF-8
LOCALE           : en_GB.UTF-8

pandas           : 1.0.3
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 46.1.3.post20200330
Cython           : 0.29.15
pytest           : 5.4.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.5.0
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.1
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         : None
pandas_gbq       : None
pyarrow          : 0.15.1
pytables         : None
pytest           : 5.4.1
pyxlsb           : None
s3fs             : None
scipy            : 1.2.1
sqlalchemy       : 1.3.16
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
