BUG: concatenation on monthstart indexes in wrong timezone fails
- [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.

---

#### Code Sample

```python
import pandas as pd

kwargs = {"start": "2020-01-01", "tz": "CET", "freq": "1MS", "closed": "left"}

index_one_year = pd.date_range(end="2021-01-01", **kwargs).tz_convert("UTC")
df_one_year = pd.DataFrame(data={"full": [1] * len(index_one_year)}, index=index_one_year)

index_one_month_more = pd.date_range(end="2021-02-01", **kwargs).tz_convert("UTC")
df_one_month_more = pd.DataFrame(
    data={"one_month_more": [1] * len(index_one_month_more)}, index=index_one_month_more
)


pd.concat([df_one_year, df_one_year], axis=1)  # works
pd.concat([df_one_year, df_one_month_more], axis=1)  # raise:
# ValueError: Inferred frequency M from passed values does not conform to passed frequency MS
```

#### Problem description

<details>
<summary><i>Visualize the two dataframes</i></summary>

```python
                           full
2019-12-31 23:00:00+00:00     1
2020-01-31 23:00:00+00:00     1
2020-02-29 23:00:00+00:00     1
2020-03-31 22:00:00+00:00     1
2020-04-30 22:00:00+00:00     1
2020-05-31 22:00:00+00:00     1
2020-06-30 22:00:00+00:00     1
2020-07-31 22:00:00+00:00     1
2020-08-31 22:00:00+00:00     1
2020-09-30 22:00:00+00:00     1
2020-10-31 23:00:00+00:00     1
2020-11-30 23:00:00+00:00     1
```

```python
                           one_month_more
2019-12-31 23:00:00+00:00               1
2020-01-31 23:00:00+00:00               1
2020-02-29 23:00:00+00:00               1
2020-03-31 22:00:00+00:00               1
2020-04-30 22:00:00+00:00               1
2020-05-31 22:00:00+00:00               1
2020-06-30 22:00:00+00:00               1
2020-07-31 22:00:00+00:00               1
2020-08-31 22:00:00+00:00               1
2020-09-30 22:00:00+00:00               1
2020-10-31 23:00:00+00:00               1
2020-11-30 23:00:00+00:00               1
2020-12-31 23:00:00+00:00               1
```

</details>

A workaround is to convert dataframes back to the timezone "CET" where the inferred_freq is the correct one.

Still, I would have expected no issues on this concatenation, even when the timezone is UTC.

#### Expected Output

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

<details>

INSTALLED VERSIONS
------------------
commit           : 2cb96529396d93b46abab7bbc73a208e708c642e
python           : 3.9.2.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 20.4.0
Version          : Darwin Kernel Version 20.4.0: Thu Apr 22 21:46:47 PDT 2021; root:xnu-7195.101.2~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.4
numpy            : 1.20.1
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 52.0.0.post20210125
Cython           : 0.29.22
pytest           : 6.1.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.6 (dt dec pq3 ext lo64)
jinja2           : 2.11.3
IPython          : 7.21.0
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          : 3.0.0
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : 1.3.23
tables           : None
tabulate         : 0.8.9
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
