BUG: Fix Series.reindex losing values when reindexing to MultiIndex
- [X] closes #60923
- [X ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [X] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [X] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [X] Added an entry in the latest `doc/source/whatsnew/v3.0.0.rst` file if fixing a bug or adding a new feature.

##Series.reindex()

#Before
```
# Create a Series with a named Index
series = pd.Series([26.73, 24.255], index=pd.Index([81, 82], name='a'))

# Create a MultiIndex with level names 'a', 'b', 'c'
target = pd.MultiIndex.from_product(
    [[81, 82], [np.nan], ["2018-06-01", "2018-07-01"]], 
    names=["a", "b", "c"]
)

# This would incorrectly set all values to NaN
series.reindex(target)
# a   b    c         
# 81  NaN  2018-06-01   NaN
#          2018-07-01   NaN
# 82  NaN  2018-06-01   NaN
#          2018-07-01   NaN

# But this works correctly
series.reindex(target, level="a")
# a   b    c         
# 81  NaN  2018-06-01    26.73
#          2018-07-01    26.73
# 82  NaN  2018-06-01    24.255
#          2018-07-01    24.255

```

#After
```
# Same setup as before
series = pd.Series([26.73, 24.255], index=pd.Index([81, 82], name='a'))
target = pd.MultiIndex.from_product(
    [[81, 82], [np.nan], ["2018-06-01", "2018-07-01"]], 
    names=["a", "b", "c"]
)

# Now both produce the same correct result
series.reindex(target)  # Automatically detects level='a'
# a   b    c         
# 81  NaN  2018-06-01    26.73
#          2018-07-01    26.73
# 82  NaN  2018-06-01    24.255
#          2018-07-01    24.255

```

##Datafram.reindex()



```
df = pd.DataFrame({
    'value': [26.73, 24.255],
    'other': ['A', 'B']
}, index=pd.Index([81, 82], name='a'))

target = pd.MultiIndex.from_product(
    [[81, 82], [np.nan], ["2018-06-01", "2018-07-01"]], 
    names=["a", "b", "c"]
)
```

Before
```
df.reindex(index = target)
                   value other
a  b   c
81 NaN 2018-06-01    NaN   NaN
       2018-07-01    NaN   NaN
82 NaN 2018-06-01    NaN   NaN
       2018-07-01    NaN   NaN
```

After

```
df.reindex(index = target)
                    value other
a  b   c
81 NaN 2018-06-01  26.730     A
       2018-07-01  26.730     A
82 NaN 2018-06-01  24.255     B
       2018-07-01  24.255     B
```

> * reviewers



> Does `DataFrame.reindex` also need the same handling?

Yes, Dataframe with single index is having the same issue
```
df = pd.DataFrame({
    'value': [26.73, 24.255],
    'other': ['A', 'B']
}, index=pd.Index([81, 82], name='a'))

# Create a MultiIndex with level names 'a', 'b', 'c'
target = pd.MultiIndex.from_product(
    [[81, 82], [np.nan], ["2018-06-01", "2018-07-01"]], 
    names=["a", "b", "c"]
)



df.reindex(target)
                   value other
a  b   c
81 NaN 2018-06-01    NaN   NaN
       2018-07-01    NaN   NaN
82 NaN 2018-06-01    NaN   NaN
       2018-07-01    NaN   NaN

df.reindex(target, level="a")
                    value other
a  b   c
81 NaN 2018-06-01  26.730     A
       2018-07-01  26.730     A
82 NaN 2018-06-01  24.255     B
       2018-07-01  24.255     B
```


How its the same scenario for multiindex, reindex only works if all index are matching. Infact specifying level for multiIndex dataframe is raising TypeError

```
raise TypeError("Join on level between two MultiIndex objects is ambiguous")
TypeError: Join on level between two MultiIndex objects is ambiguous
```

```
    source_idx = pd.MultiIndex.from_product(
        [[81, 82], ["2018-06-01"]],
        names=["a", "c"]
    )
    df = pd.DataFrame(
        {"value": [26.73, 24.255]},
        index=source_idx
    )

    # Create target with same level names but different structure
    target_idx = pd.MultiIndex.from_product(
        [[81, 82], [np.nan], ["2018-06-01", "2018-07-01"]],
        names=["a", "b", "c"]
    )

    
>>> df.reindex(target_idx)  # Reindexing doesnt copy matching index values
                   value
a  b   c
81 NaN 2018-06-01    NaN
       2018-07-01    NaN
82 NaN 2018-06-01    NaN
       2018-07-01    NaN
```

Reindex MultiIndex dataframe works iff all indexes match.

I will leave the multiIndex dataframe functionality as is and address the issue in single index dataframe like the example above. lmk what you think. @mroeschke could you please review it when you get a chance @mroeschke , sorry for tagging again. I have addressed all comments from what I can see,  but still seeing "Changes Requested" "[mroeschke](https://github.com/mroeschke) Requested changes". Have I missed addressing any of your comments? Do you mind pointing which comment is not being addressed. 
 Thanks @Roline-Stapny 