BUG: wrong processing of wintertime
### 

- [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
import pandas as pd
import pytz
from datetime import datetime

pd.show_versions()
date_list = ['2016-03-27 01:55:00', '2016-03-27 02:00:00', '2016-03-27 03:00:00']
df = pd.DataFrame()
df['date'] = date_list
df.date = pd.to_datetime(df.date, format='%Y-%m-%d %H:%M:%S')

timezone = pytz.timezone('Europe/Amsterdam')
df['date2'] = df.date.apply(lambda x: timezone.localize(x))
print(df)

date_list2 = [timezone.localize(datetime.strptime(i, '%Y-%m-%d %H:%M:%S')) for i in date_list]
print(date_list2)

df['date3'] = date_list2
print(df)
```


### Issue Description

By adding timezone information with pytz, it should only add the hours that differ from UTC, however, when doing so through pandas, it changes the time itself too. This does not happen when using datetime and pytz outside of pandas.
It only does this when going from winter time to summer time (march) and it causes duplicated timestamps which should not be there.

These timestamps:
['2016-03-27 01:55:00', '2016-03-27 02:00:00', '2016-03-27 03:00:00']

are transformed by pandas to:

**                 date                     date2
0 2016-03-27 01:55:00 2016-03-27 01:55:00+01:00
1 2016-03-27 02:00:00 2016-03-27 03:00:00+02:00
2 2016-03-27 03:00:00 2016-03-27 03:00:00+02:00**

Where both the 02:00:00 hour an the 03:00:00 hour are transformed to 03:00:00 + 02:00, causing the duplication.
This happens with all times between 02:00:00 and 03:00:00 during the switch from winter to summertime.

The expected values (as given when using datetime with pytz outside of pandas):

- datetime.datetime(2016, 3, 27, 1, 55, tzinfo=<DstTzInfo 'Europe/Amsterdam' CET+1:00:00 STD>), 
- datetime.datetime(2016, 3, 27, 2, 0, tzinfo=<DstTzInfo 'Europe/Amsterdam' CET+1:00:00 STD>), 
- datetime.datetime(2016, 3, 27, 3, 0, tzinfo=<DstTzInfo 'Europe/Amsterdam' CEST+2:00:00 DST>)

As soon as I add the correct values (as processed by datetime/pytz) as a column to a pandas dataframe, it shows it wrong again. Where both datetime.datetime(2016, 3, 27, 2, 0, tzinfo=<DstTzInfo 'Europe/Amsterdam' CET+1:00:00 STD>) and
datetime.datetime(2016, 3, 27, 3, 0, tzinfo=<DstTzInfo 'Europe/Amsterdam' CEST+2:00:00 DST>) are shown as the same time.

### Expected Behavior

The expected values (as given when using datetime with pytz outside of pandas):

- datetime.datetime(2016, 3, 27, 1, 55, tzinfo=<DstTzInfo 'Europe/Amsterdam' CET+1:00:00 STD>), 
- datetime.datetime(2016, 3, 27, 2, 0, tzinfo=<DstTzInfo 'Europe/Amsterdam' CET+1:00:00 STD>), 
- datetime.datetime(2016, 3, 27, 3, 0, tzinfo=<DstTzInfo 'Europe/Amsterdam' CEST+2:00:00 DST>)

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit           : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python           : 3.9.6.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.18363
machine          : AMD64
processor        : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_United States.1252

pandas           : 1.3.4
numpy            : 1.21.4
pytz             : 2021.3
dateutil         : 2.8.2
pip              : 21.1.2
setuptools       : 57.0.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
