BUG: Hexbin creates inconsistent size of hexagons for nearly the same data, depending on distance from axis limits
### 

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

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


### Reproducible Example

```python
import pandas as pd
from io import StringIO
csv_data = "x,y\n1,1\n1,2\n2,1\n1,9\n9,1"
csv_data2 = "x,y\n1,1\n1,2\n2,1\n1,3\n3,1"
pd.read_csv(StringIO(csv_data)).plot(kind='hexbin',x='x',y='y',\
                   xlim=[-0.5,10.5],ylim=[0,10.5],\
                   gridsize=[10,10],sharex=False)
pd.read_csv(StringIO(csv_data2)).plot(kind='hexbin',x='x',y='y',\
                   xlim=[-0.5,10.5],ylim=[0,10.5],\
                   gridsize=[10,10],sharex=False)
```


### Issue Description

csv_data gets plotted with big hexagons touching each other as expected.  csv_data2 gets plotted with much smaller hexagons, probably because there is no data near the right xlim, upper ylim.

### Expected Behavior

I was expecting the same size of hexagon for those two datasets

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit           : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python           : 3.7.10.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.19.0-18-cloud-amd64
Version          : #1 SMP Debian 4.19.208-1 (2021-09-29)
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : None
LANG             : C.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.3.4
numpy            : 1.19.5
pytz             : 2021.3
dateutil         : 2.8.2
pip              : 21.3
setuptools       : 58.2.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 3.0.2
IPython          : 7.28.0
pandas_datareader: None
bs4              : None
bottleneck       : 1.3.2
fsspec           : 2021.10.1
fastparquet      : None
gcsfs            : 2021.10.1
matplotlib       : 3.4.3
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 5.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.7.1
sqlalchemy       : 1.4.26
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : 0.54.1
</details>
