BUG: CategoricalIndex.searchsorted doesn't return a scalar if input was scalar
`CategoricalIndex.searchsorted` returns the wrong shape for scalar input. Numpy arrays and all other index types return a scalar if the input is a scalar, but the `CategoricalIndex` does not

For example
```
>>> import numpy as np
>>> np.array([1, 2, 3]).searchsorted(1)
0
>>> np.array([1, 2, 3]).searchsorted([1])
array([0])
>>> import pandas as pd
>>> pd.Index([1, 2, 3]).searchsorted(1)
0
>>> pd.Index([1, 2, 3]).searchsorted([1])
array([0])
```

This issue also affects slicing on sorted/ordered categoricals, which is why I've written another test for the slicing.


- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
- [ ] example in categoricals.rst
# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/21019?src=pr&el=h1) Report
> Merging [#21019](https://codecov.io/gh/pandas-dev/pandas/pull/21019?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/0c65c57a279e755ab7093db925d1e580f9878dae?src=pr&el=desc) will **increase** coverage by `<.01%`.
> The diff coverage is `100%`.

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```diff
@@            Coverage Diff             @@
##           master   #21019      +/-   ##
==========================================
+ Coverage   91.85%   91.85%   +<.01%     
==========================================
  Files         153      153              
  Lines       49549    49551       +2     
==========================================
+ Hits        45512    45514       +2     
  Misses       4037     4037
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.25% <100%> (ø)` | :arrow_up: |
| #single | `41.87% <0%> (-0.01%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/21019?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/indexes/category.py](https://codecov.io/gh/pandas-dev/pandas/pull/21019/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvaW5kZXhlcy9jYXRlZ29yeS5weQ==) | `97.03% <ø> (ø)` | :arrow_up: |
| [pandas/core/arrays/categorical.py](https://codecov.io/gh/pandas-dev/pandas/pull/21019/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvYXJyYXlzL2NhdGVnb3JpY2FsLnB5) | `95.7% <100%> (+0.01%)` | :arrow_up: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/21019?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/21019?src=pr&el=footer). Last update [0c65c57...04ca52f](https://codecov.io/gh/pandas-dev/pandas/pull/21019?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 does this have an associated issue? (pls do a search if not) Could this be a reason the slowness seen in #20395? (i.e. searchsorted returning wrong type, and then pandas taking a different, slower, code path...) @jreback The only issue I could find which seems to be related is https://github.com/pandas-dev/pandas/issues/9748 where an open TODO is the slicing on categoricals. From what I can see slicing is still not working for integers, though.

@topper-123 I don't think these are related. You should only hit the `searchsorted` code path during range slicing, e.g. `df[1:2]` @fjetter, actually that operation is/should be slicing and use searchsorted, as the index is a monotonic index, and ``'b'`` is not unique.

I'll look into that again with this angle. I won't high-jack this thread anymore. I refactored the tests and hope the intention is a bit clearer now. Slicing of the categorical should behave similar to a ordinary index (at least if it is ordered) Tests fail because of an import error of `geopandas`. I have no idea what might cause this, though. pls rebase on master (ci failures are fixed) Hey @fjetter , want to follow this to the door? can you merge master and can see where this is