BUG: some read_excel engines still load trailing blank cells
- [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.

---

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

```python

pd.read_excel('trailing_blanks.xlsx').shape

```
Test files available [here](https://github.com/ahawryluk/pandas/tree/trailing_blanks).

![trailing_blanks](https://user-images.githubusercontent.com/50434302/116117972-172d5580-a67a-11eb-99c7-a1f0fa499f8d.png)


I loaded this sample spreadsheet in all five filetypes to see what shape is returned:

filetype | pandas 1.2.x | master
-|-|-
xls | (3, 3) | (3, 3)
xlsx | (11, 6) | (3, 6)
xlsm | (11, 6) | (3, 6)
xlsb | (11, 6) | (11, 6)
ods | (3, 3) | (3, 3)

#### Expected Output

`(3, 3)`

#### Problem description

Spreadsheet files can contain cells with no values, such as the formatted blank cells shown below. In the worst cases, the cell used to contain a value or formatting but currently contains neither and is thus invisible to the user of the spreadsheet application. The trailing rows of NaNs in xls[x|m] files were recently resolved in #39547 by @rhshadrach but the additional columns still remain. In a case of the size pictured here, it's a minor annoyance, but I've come across spreadsheets in the wild where one more cells were unintentionally created on row `2**20` or column `2**14`, which can create a severe performance issues. To fix the performance issue, the empty rows/columns must be trimmed before the data is passed to TextParser.

I suspect that these bug reports are related:
#40569 
#40976



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

<details>

INSTALLED VERSIONS
------------------
commit           : 1e912821c03f08784cd12c046999c2033d94dd85
python           : 3.8.6.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.8.0-50-generic
Version          : #56-Ubuntu SMP Mon Apr 12 17:18:36 UTC 2021
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_CA.UTF-8
LOCALE           : en_CA.UTF-8

pandas           : 1.3.0.dev0+1412.g1e912821c0
numpy            : 1.19.5
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 49.6.0.post20210108
Cython           : 0.29.21
pytest           : 6.2.2
hypothesis       : 6.1.1
sphinx           : 3.4.3
blosc            : None
feather          : None
xlsxwriter       : 1.3.7
lxml.etree       : 4.6.2
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.20.0
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : 1.3.2
fsspec           : 0.8.5
fastparquet      : 0.5.0
gcsfs            : 0.7.1
matplotlib       : 3.3.4
numexpr          : 2.7.2
odfpy            : None
openpyxl         : 3.0.6
pandas_gbq       : None
pyarrow          : 3.0.0
pyxlsb           : 1.0.8
s3fs             : 0.5.2
scipy            : 1.6.0
sqlalchemy       : 1.3.23
tables           : 3.6.1
tabulate         : 0.8.7
xarray           : 0.16.2
xlrd             : 2.0.1
xlwt             : 1.3.0
numba            : 0.52.0

</details>