BUG: parsing ISO8601 string with format= and timezone name fails

[MarcoGorelli]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

In pandas 1.5.1:

import pandas as pd
from pandas import *
import sys
import datetime as dt

print(dt.datetime.strptime('2020-01-01 00:00:00UTC', '%Y-%m-%d %H:%M:%S%Z'))  # works!
to_datetime('2020-01-01 00:00:00UTC', format='%Y-%m-%d %H:%M:%S%Z')  # crashes!

Issue Description

The ISO8601 fastpath in

https://github.com/pandas-dev/pandas/blob/6ec3d8f9dfd5dc89a2e9695ee4e01d8c280ebe2c/pandas/_libs/tslibs/src/datetime/np_datetime_strings.c

has no way to deal with '%Z' formatted-strings. They shouldn't get there in the first place.

Hence, '%Z' should be excluded from

pandas/pandas/_libs/tslibs/parsing.pyx

Lines 886 to 903 in 634196a

	def format_is_iso(f: str) -> bint:
	"""
	Does format match the iso8601 set that can be handled by the C parser?
	Generally of form YYYY-MM-DDTHH:MM:SS - date separator can be different
	but must be consistent. Leading 0s in dates and times are optional.
	"""
	iso_template = '%Y{date_sep}%m{date_sep}%d{time_sep}%H:%M:%S{micro_or_tz}'.format
	excluded_formats = ['%Y%m%d', '%Y%m', '%Y']
	for date_sep in [' ', '/', '\\', '-', '.', '']:
	for time_sep in [' ', 'T']:
	for micro_or_tz in ['', '%z', '%Z', '.%f', '.%f%z', '.%f%Z']:
	if (iso_template(date_sep=date_sep,
	time_sep=time_sep,
	micro_or_tz=micro_or_tz,
	).startswith(f) and f not in excluded_formats):
	return True
	return False

Expected Behavior

In [1]: to_datetime('2020-01-01 00:00:00UTC', format='%Y-%m-%d %H:%M:%S%Z')
Out[1]: Timestamp('2020-01-01 00:00:00+0000', tz='UTC')

Installed Versions

INSTALLED VERSIONS

commit : 91111fd
python : 3.10.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.102.1-microsoft-standard-WSL2
Version : #1 SMP Wed Mar 2 00:30:59 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8

pandas : 1.5.1
numpy : 1.23.3
pytz : 2022.2.1
dateutil : 2.8.1
setuptools : 65.3.0
pip : 22.2.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.5.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : None
fastparquet : None
fsspec : 2022.10.0
gcsfs : None
matplotlib : 3.6.1
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : 2022.10.0
scipy : 1.9.1
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None
None

[MarcoGorelli]

This was introduced with https://github.com/pandas-dev/pandas/pull/42068/files