BUG: fix styler cell_ids arg so that blank style is ignored on False
- [x] closes #35586 
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry

Can you add a test case for the problem you are trying to solve? As reviewers this is typically the first thing we look for > Can you add a test case for the problem you are trying to solve? As reviewers this is typically the first thing we look for

Do you mean in the issue or a test in code?
```
import pandas as pd  # NOTE VERSION 1.1.0
from pandas.io.formats.style import Styler
def highlight_max(s):
    x = s == s.max()
    x = x.replace(False, '')
    x = x.replace(True, 'background-color: yellow; color: brown')
    return x
df = pd.DataFrame(data=[[0,1], [1,0]])
s = Styler(df, uuid='_', cell_ids=False)
s.apply(highlight_max)
s.render()
```
yields...
```
('<style  type="text/css" >\n'
 '#T__row0_col1,#T__row1_col0{\n'
 '            background-color:  yellow;\n'
 '             color:  brown;\n'
 '        }</style><table id="T__" ><thead>    <tr>        <th class="blank '
 'level0" ></th>        <th class="col_heading level0 col0" >0</th>        <th '
 'class="col_heading level0 col1" >1</th>    </tr></thead><tbody>\n'
 '                <tr>\n'
 '                        <th id="T__level0_row0" class="row_heading level0 '
 'row0" >0</th>\n'
 '                        <td id="T__row0_col0" class="data row0 col0" '
 '>0</td>\n'
 '                        <td id="T__row0_col1" class="data row0 col1" '
 '>1</td>\n'
 '            </tr>\n'
 '            <tr>\n'
 '                        <th id="T__level0_row1" class="row_heading level0 '
 'row1" >1</th>\n'
 '                        <td id="T__row1_col0" class="data row1 col0" '
 '>1</td>\n'
 '                        <td id="T__row1_col1" class="data row1 col1" '
 '>0</td>\n'
 '            </tr>\n'
 '    </tbody></table>')
```

This is clearly erroneous since it has added the id tag to every data cell when (0,0) and (1,1) should be ignored.

Note that the correct output was produced using the same code on pandas version 1.0.4.

The bug fix in this PR yields the code:
```
('<style  type="text/css" >\n'
 '#T__row0_col1,#T__row1_col0{\n'
 '            background-color:  yellow;\n'
 '             color:  brown;\n'
 '        }</style><table id="T__" ><thead>    <tr>        <th class="blank '
 'level0" ></th>        <th class="col_heading level0 col0" >0</th>        <th '
 'class="col_heading level0 col1" >1</th>    </tr></thead><tbody>\n'
 '                <tr>\n'
 '                        <th id="T__level0_row0" class="row_heading level0 '
 'row0" >0</th>\n'
 '                        <td  class="data row0 col0" >0</td>\n'
 '                        <td id="T__row0_col1" class="data row0 col1" '
 '>1</td>\n'
 '            </tr>\n'
 '            <tr>\n'
 '                        <th id="T__level0_row1" class="row_heading level0 '
 'row1" >1</th>\n'
 '                        <td id="T__row1_col0" class="data row1 col0" '
 '>1</td>\n'
 '                        <td  class="data row1 col1" >0</td>\n'
 '            </tr>\n'
 '    </tbody></table>')
```

which restores correct functionality since the id tags are removed where appropriate.
 @attack68 in the code - see the contributing guide https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#test-driven-development-code-writing

>pandas is serious about testing and strongly encourages contributors to embrace test-driven development (TDD). This development process “relies on the repetition of a very short development cycle: first the developer writes an (initially failing) automated test case that defines a desired improvement or new function, then produces the minimum amount of code to pass that test.” So, before actually writing any code, you should write your tests. Often the test can be taken from the original GitHub issue. However, it is always worth considering additional use cases and writing corresponding tests.
>
>Adding tests is one of the most common requests after code is pushed to pandas. Therefore, it is worth getting in the habit of writing tests ahead of time so this is never an issue.
>
>Like many packages, pandas uses pytest and the convenient extensions in numpy.testing. I wrote what I thought was a very simple test, demonstrating the issue and impact, in the necessary format.
However `Linting` failed.
Any advice on the reason for the test failure? New test failures appeared after the merge by simon. These seem unrelated to my code changes. Any advice? thanks @attack68  @MeeseeksDev backport to 1.1.x