BUG: datetime.date objects don't get parsed for non-ISO formats

[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

to_datetime([dt.date(2000, 1, 2)], format='%Y-%m-%d %H:%M:%S%z')  # works!
to_datetime([dt.date(2000, 1, 2)], format='%Y-%d-%m %H:%M:%S%z')  # fails!

Issue Description

Traceback (most recent call last):
  File "t.py", line 45, in <module>
    to_datetime([dt.date(2000, 1, 2)], format='%Y-%d-%m %H:%M:%S%z')
  File "/home/marcogorelli/pandas-dev/pandas/core/tools/datetimes.py", line 1112, in to_datetime
    result = convert_listlike(argc, format)
  File "/home/marcogorelli/pandas-dev/pandas/core/tools/datetimes.py", line 438, in _convert_listlike_datetimes
    res = _to_datetime_with_format(
  File "/home/marcogorelli/pandas-dev/pandas/core/tools/datetimes.py", line 544, in _to_datetime_with_format
    res = _array_strptime_with_fallback(
  File "/home/marcogorelli/pandas-dev/pandas/core/tools/datetimes.py", line 478, in _array_strptime_with_fallback
    result, timezones = array_strptime(
  File "pandas/_libs/tslibs/strptime.pyx", line 189, in pandas._libs.tslibs.strptime.array_strptime
    raise ValueError(f"time data '{val}' does not match "
ValueError: time data '2000-01-02' does not match format '%Y-%d-%m %H:%M:%S%z' (match)

Expected Behavior

DatetimeIndex(['2000-01-02'], dtype='datetime64[ns]', freq=None)

Installed Versions

INSTALLED VERSIONS

commit : e93ee07
python : 3.8.15.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 : 2.0.0.dev0+863.ge93ee07729
numpy : 1.23.5
pytz : 2022.6
dateutil : 2.8.2
setuptools : 65.5.1
pip : 22.3.1
Cython : 0.29.32
pytest : 7.2.0
hypothesis : 6.59.0
sphinx : 4.5.0
blosc : None
feather : None
xlsxwriter : 3.0.3
lxml.etree : 4.9.1
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : 2.9.3
jinja2 : 3.0.3
IPython : 8.7.0
pandas_datareader: 0.10.0
bs4 : 4.11.1
bottleneck : 1.3.5
brotli :
fastparquet : 2022.11.0
fsspec : 2021.11.0
gcsfs : 2021.11.0
matplotlib : 3.6.2
numba : 0.56.4
numexpr : 2.8.3
odfpy : None
openpyxl : 3.0.10
pandas_gbq : 0.17.9
pyarrow : 9.0.0
pyreadstat : 1.2.0
pyxlsb : 1.0.10
s3fs : 2021.11.0
scipy : 1.9.3
snappy :
sqlalchemy : 1.4.44
tables : 3.7.0
tabulate : 0.9.0
xarray : 2022.11.0
xlrd : 2.0.1
zstandard : 0.19.0
tzdata : None
qtpy : None
pyqt5 : None
None