BUG: frequency attribute lost if datetimeindex is localized
- [x] I have checked that this issue has not already been reported.
(Might be related to [33647](https://github.com/pandas-dev/pandas/issues/33647), though there localization does not seem to play a role)

- [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
import pandas as pd

s = pd.Series([2,5,3], index=pd.date_range('2020-01-01', periods=3, 
                                           freq='M', tz='Europe/Berlin'))
s.index #freq correctly set
```
```
DatetimeIndex(['2020-01-31 00:00:00+01:00', '2020-02-29 00:00:00+01:00',
               '2020-03-31 00:00:00+02:00'],
              dtype='datetime64[ns, Europe/Berlin]', name='ts', freq='M')
```
Then changing to `MultiIndex`:
```python
s.index = pd.MultiIndex.from_arrays([s.index, s.index.month])
s.index.get_level_values(0)   #freq has been lost
```
```
DatetimeIndex(['2020-01-31 00:00:00+01:00', '2020-02-29 00:00:00+01:00',
               '2020-03-31 00:00:00+02:00'],
              dtype='datetime64[ns, Europe/Berlin]', name='ts', freq=None
```
Notice the `freq=None`.

#### Problem description

In the snipped above, the `freq` attribute of the `DateTimeIndex` is lost when a `MultiIndex` is created from it.

The problem seems to stem from the localization. It's equally present in DataFrames and when using `set_index`/`tz_localize` to promote a normal datetime column to index.

#### Expected Output

I expect the `freq` attribute to persist, as it does in this snippet, when I do not localize the index:

```python
s2 = pd.Series([2,5,3], index=pd.date_range('2020-01-01', periods=3, 
                                            freq='M')) #NB no localization
s2.index = pd.MultiIndex.from_arrays([s2.index, s2.index.month]) #same as above
s2.index.get_level_values(0)
```
```
DatetimeIndex(['2020-01-31', '2020-02-29', '2020-03-31'], 
              dtype='datetime64[ns]', freq='M')
```



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

<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.2.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en
LOCALE           : de_DE.cp1252

pandas           : 1.0.3
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 46.1.3.post20200330
Cython           : None
pytest           : 5.4.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : 7.13.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.3
numexpr          : None
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.4.1
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
