BUG: `pandas.to_datetime` raises exception when more than 50 values needs coercion to NaT
### 

- [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 datetime, pytz
    
# t is a date after 2262 that should be coerced into NaT
t = datetime.datetime(3000, 1, 1, 0, 0, 0, 0, pytz.UTC)

# lists with many such dates
l50 = [t] * 50
l51 = [t] * 51

# pd.to_datetime crashes if the list is longer that 50

# this is fine
print(pd.to_datetime(l50, utc=True, errors='coerce'))  

# this crashes
print(pd.to_datetime(l51, utc=True, errors='coerce'))
```


### Issue Description

Background (irrelevant for reproducing the bug, but good to know):
A google bigquery gives me dataframe with a column with datetime values with year=9999, 
and I store the result as a parquet file. When I read it back, `read_parquet` crashes.
I traced it down to the following problem in pd.to_datetime.

The issue:
if pd.datetime needs to convert more than 50 datetime values with tzinfo set, and those values are above year 2262 (the maximal date for pd.Timestamp) and should be coerced into NaT, then pd.to_datetime crashes with a raised exception.  It works fine if the series is smaller than 51 items.



### Expected Behavior

The expected behavior is that pd.to_datetime should return a list of NaT values also for Sequences longer than 50 items.

### Installed Versions

<details>

------------------
commit           : 73c68257545b5f8530b7044f56647bd2db92e2ba
python           : 3.7.10.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.19.0-17-cloud-amd64
Version          : #1 SMP Debian 4.19.194-3 (2021-07-18)
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : C.UTF-8
LOCALE           : None.None

pandas           : 1.3.3
numpy            : 1.19.5
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.2.4
setuptools       : 58.0.4
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.27.0
pandas_datareader: None
bs4              : None
bottleneck       : 1.3.2
fsspec           : 2021.08.1
fastparquet      : None
gcsfs            : 2021.08.1
matplotlib       : 3.4.3
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 5.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.7.1
sqlalchemy       : 1.4.25
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : 0.54.0
</details>
