Consistent dtype output for element wise operations on empty dataframes
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
s = pd.Series([], dtype='datetime64[ns, UTC]')
df = pd.concat([s, s], axis=1) # empty dataframe of two columns of datetime dtype
# want the minimum of the two datetime columns element wise
min_dates = df.min(axis=1) # output series is of dtype float64

# it's fine if dataframe is non-empty
import pandas as pd
s = pd.Series(pd.to_datetime([f'2020-01-0{i}' for i in range(1, 10)], utc=True))
df = pd.concat([s, s], axis=1) # non-empty dataframe of two columns of datetime dtype
min_dates = df.min(axis=1) # output series is of dtype datetime64[ns, UTC]
```
#### Problem description

When dataframe is empty, doing a `.min(axis=1)` outputs a series of dtype `float64` even though all columns were dtype `datetime64`.
Should this be consitent on dtypes and output `min_dates` with dtype `datetime64[ns, UTC]`.

This is only an issue when the dataframe is empty.
If the dataframe is not empty, the dtype is conserved and min_dates outputs with dtype `datetime64[ns, UTC]`.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 3.10.0-862.3.3.el7.x86_64
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None
pandas           : 1.0.2
numpy            : 1.16.5
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 20.0.2
setuptools       : 41.4.0
Cython           : 0.29.13
pytest           : 5.0.1
hypothesis       : None
sphinx           : 2.1.2
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.3 (dt dec pq3 ext lo64)
jinja2           : 2.10.1
IPython          : 7.7.0
pandas_datareader: None
bs4              : 4.8.1
bottleneck       : None
fastparquet      : None
gcsfs            : 0.3.1
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : 2.6.9
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.15.1
pytables         : None
pytest           : 5.0.1
pyxlsb           : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.8
tables           : 3.5.2
tabulate         : None
xarray           : 0.14.0
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : 0.46.0
