df.apply(lambda x: x.name, axis=1) lose timezone info when df.index is tz-aware.
#### Copy-pastable Code Sample

```python
import pandas as pd

df = pd.DataFrame(
    data=[0, 1, 2],
    index=[
        pd.Timestamp('2019-03-15 12:34:31.909000+0000', tz='UTC'),
        pd.Timestamp('2019-03-15 12:34:34.359000+0000', tz='UTC'),
        pd.Timestamp('2019-03-15 12:34:34.660000+0000', tz='UTC'),
    ],
)

df.apply(lambda x: x.name, axis=1)
```
Outputs:
```
2019-03-15 12:34:31.909000+00:00   2019-03-15 12:34:31.909
2019-03-15 12:34:34.359000+00:00   2019-03-15 12:34:34.359
2019-03-15 12:34:34.660000+00:00   2019-03-15 12:34:34.660
dtype: datetime64[ns]
```

#### Problem description

When `apply`ing a function on each row (`axis=1`) the index of that row becomes name of the serie passed to the function. Any type conversion, such as dropping timezone, is unexpected.

Workaround: `df.reset_index().apply(lambda x: x["time"], axis=1)`


#### Expected Output

Expect the timezones to be kept. E.g.

```
2019-03-15 12:34:31.909000+00:00   2019-03-15 12:34:31.909+00:00
2019-03-15 12:34:34.359000+00:00   2019-03-15 12:34:34.359+00:00
2019-03-15 12:34:34.660000+00:00   2019-03-15 12:34:34.660+00:00
dtype: datetime64[ns, UTC]
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-62-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_DK.UTF-8
LOCALE           : en_DK.UTF-8

pandas           : 0.25.1
numpy            : 1.17.2
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.3
setuptools       : 41.2.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.8.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
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>
