BUG: fillna with limit and no method ignores axis='columns'
- [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.

---

**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
In [6]: df = pd.DataFrame([[np.nan, 2, np.nan, 0],
   ...:                    [3, 4, np.nan, 1],
   ...:                    [np.nan, np.nan, np.nan, 5],
   ...:                    [np.nan, 3, np.nan, 4]],
   ...:                    columns=list('ABCD'))

In [7]: df
Out[7]: 
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5
3  NaN  3.0 NaN  4

In [8]: df.fillna(axis='columns', value=100, limit=2)
Out[8]: 
       A      B      C  D
0  100.0    2.0  100.0  0
1    3.0    4.0  100.0  1
2  100.0  100.0    NaN  5
3    NaN    3.0    NaN  4

In [9]: df.fillna(axis='columns', value=100, limit=1)
Out[9]: 
       A      B      C  D
0  100.0    2.0  100.0  0
1    3.0    4.0    NaN  1
2    NaN  100.0    NaN  5
3    NaN    3.0    NaN  4
```

#### Problem description

Based on the [description](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html) of `limit`:

>  If method is not specified, this is the maximum number of entries *along the entire axis where NaNs will be filled.* Must be greater than 0 if not None.

I would expect it to apply along the columns axis in this case, but it seems to be applied along the index. It's possible this is intentional, but if that's the case I'd like to update the documentation

#### Expected Output

```python
In [8]: df.fillna(axis='columns', value=100, limit=2)
Out[8]: 
       A      B      C  D
0  100.0    2.0  100.0  0
1    3.0    4.0  100.0  1
2  100.0  100.0    NaN  5
3  100.0    3.0  100.0  4

In [9]: df.fillna(axis='columns', value=100, limit=1)
Out[9]: 
       A      B      C  D
0  100.0    2.0    NaN  0
1    3.0    4.0  100.0  1
2  100.0    NaN    NaN  5
3  100.0    3.0    NaN  4
```

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

<details>
pandas           : 1.2.4
numpy            : 1.19.5
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.2.1
setuptools       : 49.2.1
Cython           : 0.29.13
pytest           : 4.6.11
hypothesis       : None
sphinx           : 1.8.5
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.6 (dt dec pq3 ext lo64)
jinja2           : 2.11.2
IPython          : 7.19.0
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          : 3.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.20
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None
</details>
