BUG:read_csv misleading handling of csv files containing rows with different number of delimiters
- [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
import pandas as pd
pd.read_csv('fake.csv')
```

fake.csv file
```csv
col1,col2,col3,col4,col5
a,a,a,a,a
a,a,a
```

#### Problem description

Pandas read_csv function should throw an error, or at least a warning, when a csv contains a row with a number of delimiters less than the number of delimiters contained in the header row (the first raw?).
For example, if the number of delimiters is greater an exception is thrown.

#### Expected Output

Error or warning.

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

<details>

INSTALLED VERSIONS
------------------
commit           : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python           : 3.8.5.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-42-generic
Version          : #46~18.04.1-Ubuntu SMP Fri Jul 10 07:21:24 UTC 2020
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_GB.UTF-8
LOCALE           : en_GB.UTF-8

pandas           : 1.1.1
numpy            : 1.19.1
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.2.2
setuptools       : 49.6.0.post20200814
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
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
