Call .max method on two timezone datetime columns results in nan 
#### Code Sample, a copy-pastable example if possible

This works when comparing two non-timezone datetime columns
```python
a = pd.DataFrame(
    {
        "a": [pd.Timestamp("20180101"), np.nan],
        "b": [pd.Timestamp("20190101"), pd.Timestamp("20190101")],
    }
)
a.max(axis=1)
```
Output:
0   2019-01-01
1   2019-01-01
dtype: datetime64[ns]

But it fails on time-zone datetime columns
```python
a = pd.DataFrame(
    {
        "a": [pd.Timestamp("20180101", tz="utc"), np.nan],
        "b": [pd.Timestamp("20190101", tz="utc"), pd.Timestamp("20190101", tz="utc")],
    }
)
a.max(axis=1)
```
Output:
0   NaN
1   NaN
dtype: float64

#### Problem description
I'm expecting the .max function to work consistently on both non-timezone and timezone datetime columns. But as the above results shows, somehow the comparison of timezone datetime is adjusted to float and the results are all nan.

#### Expected Output

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

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Windows
OS-release       : 7
machine          : AMD64
processor        : Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.1
numpy            : 1.16.5
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.2
setuptools       : 41.0.1
Cython           : 0.29.13
pytest           : 5.0.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : 0.4.0
xlsxwriter       : 1.1.8
lxml.etree       : 4.4.1
html5lib         : 1.0.1
pymysql          : 0.9.3
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.8.0
pandas_datareader: 0.7.4
bs4              : 4.8.0
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 2.6.2
pandas_gbq       : None
pyarrow          : 0.11.1
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.7
tables           : 3.5.2
xarray           : 0.12.1
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.1.8
