BUG: Series.apply behaves incorrectly with timezone-aware data.
- [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.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

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

```python
# For a series with tz-unaware datetime, apply behaves correctly and passes each value to the function.
pd.Series([pd.Timestamp(1)]).apply(print)

# For tz-aware data, apply injects a DatetimeIndex containing the whole series as first argument
pd.Series([pd.Timestamp(1)]).astype("datetime64[ns, UTC]").apply(print)
```

Output:
```
# First, correct:
1970-01-01 00:00:00.000000001+00:00

# Second, incorrect:
DatetimeIndex(['1970-01-01 00:00:00.000000001+00:00'], dtype='datetime64[ns, UTC]', freq=None)
1970-01-01 00:00:00.000000001+00:00
```

#### Problem description

According to [the documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html#pandas.Series.apply), `apply` should call the provided function (`print` in the dummy example) to each value in the series.
For timezone-unaware data, this works as expected. In the example, `print` is called with the timestamp only.

However, when the data is tz-aware, the function is first called with a `DatetimeIndex` containing the whole series. Only afterwards, the function is called with each value in the series.


#### Expected Output

Regardless of whether the data is tz-aware, apply should call the function only with each value in the series.

Or am I misunderstanding `apply`, and this output is actually intended?

In any case, thanks for your help!

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.9.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-76-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.3
numpy            : 1.18.2
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 9.0.1
setuptools       : 46.1.3
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : 0.999999999
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.13.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.2.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : None
tables           : 3.6.1
tabulate         : 0.8.7
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
