BUG: v1.3.0 to_datetime caching behavior changed compared to v1.2.5
- [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, a copy-pastable example

```python
# 51 rows results in missingness
df = pd.DataFrame({'date':['07.30.2020'] + ['30.07.2020']*50})
pd.to_datetime(df.date, dayfirst=True).isna().sum()

# 50 rows results in no missingness
pd.to_datetime(df.date[0:50], dayfirst=True).isna().sum()

# 51 rows with cache=False results in no missingness
pd.to_datetime(df.date, dayfirst=True, cache=False).isna().sum()
```

#### Problem description

Starting with v1.3.0 we began running into issues where pd.to_datetime would not convert all dates as expected if there are conflicting formats. It appears that the default behavior of `cache=True` behaves differently between the two versions. In v1.3.0 it seems that `cache=False` fixes the problem but that slows things down.

#### Expected Output

We would expect there to be no missingness with the default of `cache=True`.

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

<details>

INSTALLED VERSIONS
------------------
commit           : f00ed8f47020034e752baf0250483053340971b0
python           : 3.7.10.final.0
python-bits      : 64
OS               : Linux
OS-release       : 3.10.0-957.12.2.el7.x86_64
Version          : #1 SMP Tue May 14 21:24:32 UTC 2019
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.3.0
numpy            : 1.21.0
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.1.3
setuptools       : 52.0.0.post20210125
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
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          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
