BUG: `Series.map` passes `DatetimeIndex` as first argument on some series
### 

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

- [X] I have confirmed this bug exists on the master branch of pandas.


### Reproducible Example

```python
from datetime import datetime
from random import randint

import pandas as pd


def mapping_func(ts):
    assert type(ts) == pd.Timestamp


# --- works as expected ---

ts_list = [
    datetime(2021, randint(1, 12), randint(1, 28))
    for _ in range(5)
]

df01 = pd.DataFrame(ts_list, columns=["timestamps"])
df01["timestamps"].map(mapping_func)


# --- doesn't work as expected ---


ts_list_02 = [
    '2020-09-01T15:47:33.953000+00:00',
    '2020-09-02T16:57:16.547000+00:00',
    '2020-09-02T16:57:16.887000+00:00',
    '2020-09-02T16:57:12.377000+00:00',
    '2020-09-02T16:57:12.667000+00:00',
]

df02 = pd.DataFrame(ts_list_02, columns=["timestamps"])
df02["timestamps"] = pd.to_datetime(df02["timestamps"])
df02["timestamps"].map(mapping_func)


# --- Type checks ---

assert isinstance(df01["timestamps"], pd.Series)
assert isinstance(df02["timestamps"], pd.Series)

assert df01["timestamps"].dtype == df02["timestamps"].dtype
# fails because (respectively) dtype('<M8[ns]') != datetime64[ns, UTC]
```


### Issue Description

I am having a weird issued when using `Series.map(arg=fn)` on a series containing `Timestamp`s:

- In some cases the **first** time `fn` is called by map, the argument is a `DatetimeIndex` containing all timestamps in the series. Further calls pass every individual member of the series to `fn` as expected.
- In other cases it passes every individual member of the series to `fn` as expected. 

I was able to reproduce it by:

- creating one series based on a list of python `datetime`s instances (`df01` in the example - works as expected). 
- creating one series based on a list of ISO timestamps and casting the series to `Timestamp` using `pd.to_datetime` (`df02` in the example - doesn't work as expected aka. passes a full `DatetimeIndex` the first time it calls `fn`).

### Expected Behavior

```python
def mapping_func(ts):
    assert type(ts) == pd.Timestamp

ts_list = [
    datetime(2021, randint(1, 12), randint(1, 28))
    for _ in range(5)
]

df01 = pd.DataFrame(ts_list, columns=["timestamps"])
df01["timestamps"].map(mapping_func)
```
Works as expected, the `ts` parameter of `mapping_func` always being a `Timestamp` instance. 

```python
def mapping_func(ts):
    assert type(ts) == pd.Timestamp

ts_list_02 = [
    '2020-09-01T15:47:33.953000+00:00',
    '2020-09-02T16:57:16.547000+00:00',
    '2020-09-02T16:57:16.887000+00:00',
    '2020-09-02T16:57:12.377000+00:00',
    '2020-09-02T16:57:12.667000+00:00',
]

df02 = pd.DataFrame(ts_list_02, columns=["timestamps"])
df02["timestamps"] = pd.to_datetime(df02["timestamps"])
df02["timestamps"].map(mapping_func)
``` 

Should not raise an error but actually does, since the first time `mapping_func` is called, the `ts` parameter is passed a `DatetimeIndex`. 


### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit           : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python           : 3.7.9.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 20.5.0
Version          : Darwin Kernel Version 20.5.0: Sat May  8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_GB.UTF-8
LOCALE           : None.UTF-8

pandas           : 1.3.4
numpy            : 1.21.4
pytz             : 2021.3
dateutil         : 2.8.2
pip              : 21.1.3
setuptools       : 57.4.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 3.0.2
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.9.1 (dt dec pq3 ext lo64)
jinja2           : 3.0.3
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : 2021.11.0
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : 3.0.9
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : 1.4.26
tables           : None
tabulate         : 0.8.9
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
