ENH: Add keep_index as an optional argument to json_serialize
#### Is your feature request related to a problem?

I have a DataFrame with a column filled with json data. I would like concatenate a subset of that json data to the DataFrame itself while preserving the index of the DataFrame for easy concatenation.

#### Describe the solution you'd like

1. I would add `pd.Series` as a possible data input in [the definition of json_normalize]
2. Add optional keyword `keep_index`
3. If the type of the data is a Series and `keep_index` is `True`, then add set the index of the result to the index of the Series.

#### API breaking implications

As an optional feature, this does not break the API, but the preservation of the index can not take effect if the data is not a pandas Series and might have unintended effects when switching the type of data.

However, given that i doesn't affect other types of data than `pd.Series`, the default value of `keep_index` could be set to `True` without affecting the example use cases.

#### Describe alternatives you've considered

I have been using this alternative so far, simply adding the index after json_serialize.

```python
s = df['col']
result = pd.json_serialize(s)
result.index = s.index
pd.concat(df, s, axis='columns')
```

#### Additional context

This feature request is similar to #17440 but doesn't result in the same kind of output, because this doesn't add an extra column.


#### Current behaviour
```python
data = [{'id': 1, 'name': {'first': 'Coleen', 'last': 'Volk'}},
        {'name': {'given': 'Mose', 'family': 'Regner'}},
        {'id': 2, 'name': 'Faye Raker'}]
df = pd.DataFrame({"Letters":['A','B','C']})
df['data'] = data
df.index = ['a', 'b','c']
df
```
| | Letters |                                           data |
|---| --- | --- |
| a |   A | {'id': 1, 'name': {'first': 'Coleen', 'last': ... |
| b |   B | {'name': {'given': 'Mose', 'family': 'Regner'}} | 
| c |   C |                 {'id': 2, 'name': 'Faye Raker'} |

```
>>> pd.json_normalize(df['data'])
    id name.first name.last name.given name.family        name
0  1.0     Coleen      Volk        NaN         NaN         NaN
1  NaN        NaN       NaN       Mose      Regner         NaN
2  2.0        NaN       NaN        NaN         NaN  Faye Raker
>>> pd.concat([df, pd.json_normalize(df['data'])], axis='columns')
  Letters                                               data   id  ... name.given name.family        name
a       A  {'id': 1, 'name': {'first': 'Coleen', 'last': ...  NaN  ...        NaN         NaN         NaN
b       B    {'name': {'given': 'Mose', 'family': 'Regner'}}  NaN  ...        NaN         NaN         NaN
c       C                    {'id': 2, 'name': 'Faye Raker'}  NaN  ...        NaN         NaN         NaN
0     NaN                                                NaN  1.0  ...        NaN         NaN         NaN
1     NaN                                                NaN  NaN  ...       Mose      Regner         NaN
2     NaN                                                NaN  2.0  ...        NaN         NaN  Faye Raker
```

#### Expected behaviour
```
>>> pd.json_normalize(df['data'], keep_index=True)
    id name.first name.last name.given name.family        name
a  1.0     Coleen      Volk        NaN         NaN         NaN
b  NaN        NaN       NaN       Mose      Regner         NaN
c  2.0        NaN       NaN        NaN         NaN  Faye Raker
>>> pd.concat([df, pd.json_normalize(df['data'], keep_index=True)], axis='columns')
  Letters                                               data   id  ... name.given name.family        name
a       A  {'id': 1, 'name': {'first': 'Coleen', 'last': ...  1.0  ...        NaN         NaN         NaN
b       B    {'name': {'given': 'Mose', 'family': 'Regner'}}  NaN  ...       Mose      Regner         NaN
c       C                    {'id': 2, 'name': 'Faye Raker'}  2.0  ...        NaN         NaN  Faye Raker
```


[the definition of json_normalize]: https://github.com/pandas-dev/pandas/blob/6388370eb313244ba0e4cd8215dcdbfdabb7004b/pandas/io/json/_normalize.py#L113