Maximum of time series with NaT returns a dtype float64 with only NaN's
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd

show_bug = True

a = pd.date_range("2019-1-1", periods=10, freq="1D", tz="Europe/Paris")
b = pd.date_range("2019-1-3", periods=10, freq="1H", tz="Europe/Paris")

if show_bug:
    a = pd.Index([pd.NaT] + a.to_list()[1:])



multi = pd.MultiIndex.from_arrays([a, b])
m = multi.to_frame(index=False).max(axis=1)
print(m)

```
#### Problem description

When show_bug is False, no NaT's are present. This gives this output : 

```

0   2019-01-03 00:00:00+01:00
1   2019-01-03 01:00:00+01:00
2   2019-01-03 02:00:00+01:00
3   2019-01-04 00:00:00+01:00
4   2019-01-05 00:00:00+01:00
5   2019-01-06 00:00:00+01:00
6   2019-01-07 00:00:00+01:00
7   2019-01-08 00:00:00+01:00
8   2019-01-09 00:00:00+01:00
9   2019-01-10 00:00:00+01:00
dtype: datetime64[ns, Europe/Paris]


```

When show_bug is True, all it does is replacing the first value in a with a NaT.
I expect the output to be the same, except for the first row being a NaT.

Here is what I get for show_bug=True : 

```

0   NaN
1   NaN
2   NaN
3   NaN
4   NaN
5   NaN
6   NaN
7   NaN
8   NaN
9   NaN
dtype: float64


```


#### Expected Output

```

0   NaT
1   2019-01-03 01:00:00+01:00
2   2019-01-03 02:00:00+01:00
3   2019-01-04 00:00:00+01:00
4   2019-01-05 00:00:00+01:00
5   2019-01-06 00:00:00+01:00
6   2019-01-07 00:00:00+01:00
7   2019-01-08 00:00:00+01:00
8   2019-01-09 00:00:00+01:00
9   2019-01-10 00:00:00+01:00
dtype: datetime64[ns, Europe/Paris]

```

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

<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 142 Stepping 9, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.1
numpy            : 1.17.0
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.0.3
setuptools       : 40.8.0
Cython           : None
pytest           : 4.6.5
hypothesis       : 4.33.1
sphinx           : 2.2.0
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.4.1
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.7.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : 2.6.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
</details>
