BUG:  to_datetime(): Unstable default dateformat assumptions




- [ 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.

- [ x] (optional) I have confirmed this bug exists on the master branch of pandas.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

#### Code Sample, a copy-pastable example
```python

for k in range(9,17):
    dstr = '{}-07-2021 15:57:40'.format(k)  # assumed format ''%d-%m-%Y %H:%M:%S' eg. in ch/DE
    print(' dstr: {}  pd.to_datetime(): {}'.format(dstr,pd.to_datetime(dstr)))

Output:
dstr: 9-07-2021 15:57:40  pd.to_datetime(): 2021-**09**-07 15:57:40
 dstr: 10-07-2021 15:57:40  pd.to_datetime(): 2021-**10**-07 15:57:40
 dstr: 11-07-2021 15:57:40  pd.to_datetime(): 2021-**11**-07 15:57:40
 dstr: 12-07-2021 15:57:40  pd.to_datetime(): 2021-**12**-07 15:57:40
 dstr: 13-07-2021 15:57:40  pd.to_datetime(): 2021-07-13 15:57:40
 dstr: 14-07-2021 15:57:40  pd.to_datetime(): 2021-07-14 15:57:40
 dstr: 15-07-2021 15:57:40  pd.to_datetime(): 2021-07-15 15:57:40
 dstr: 16-07-2021 15:57:40  pd.to_datetime(): 2021-07-16 15:57:40


```

#### Problem description 
It would be ok if pd.to_datetime() assumed a default for parameter format, for example either US-format, or ISO-format '%Y/%m/%d %H:%M:%S'.

Anyhow as the code sample show, to_datetime() changes it's assumptions based on each input string. This is not acceptable and can be very harmful. There is no good in such dynamic parsing strategy.

I guess pd.to_datetime() assume a dateformat of  '%m-%d-%Y %H:%M:%S', once this assumption is violated, the decoding strategy changes to 

'%d-%m-%Y %H:%M:%S' instead of throwing an exception.  Such instability is no good.  pd.to_datetime()  should work according a given date format. First violation according that date format should throw an exception




#### Expected Output

Any time pd.to_datetime() should work under control of a format string, a datetime string format. This could either be a default or a parameter passed format string.

If no format string is passed as a parameter to_datetime() uses a flexible strategy. As Pandas main scope is to work with DataFrame and Series object, timeseries are a prominent application. Such flexible date format interpretation can here be very destructive. Such behaviour of  to_datetime() should not need to be expected and is NOT failsafe.

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


INSTALLED VERSIONS
------------------
commit           : 7c48ff4409c622c582c56a5702373f726de08e96
python           : 3.8.8.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : German_Switzerland.1252

pandas           : 1.2.5
numpy            : 1.20.2

<details>
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.1.3
setuptools       : 52.0.0.post20210125
Cython           : 0.29.23
pytest           : 6.2.4
hypothesis       : None
sphinx           : 4.0.2
blosc            : None
feather          : None
xlsxwriter       : 1.4.4
lxml.etree       : 4.6.3
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 3.0.1
IPython          : 7.22.0
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : 1.3.2
fsspec           : 2021.06.0
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.4
numexpr          : 2.7.3
odfpy            : None
openpyxl         : 3.0.7
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.6.2
sqlalchemy       : 1.4.19
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 2.0.1
xlwt             : 1.3.0
numba            : 0.53.1





</details>