DISCUSS: disambiguation of NA and "NA" in reprs
## Change to `str` `dtype` behaviour for missing elements

Following comments the discussion about how to handle missing `NA` scalar values in https://github.com/pandas-dev/pandas/issues/28778 I was asked to raise my question as this seperate issue. 

My rather prosaic question is how if missing `str` elements are given the value `NA`, how would I distinguish between a missing `str` value and the two-character string 'NA'?

I ask as NA is a common abbreviation for 'Not Applicable', 'North America' et al, in a way that in my experience that 'NaN' or 'Not a Number' isn't

That is, if 'NA' were generated as the default missing `str` `dtype` value, especially if introduced as change rather than as a opt-in, it risks becoming a UX developer issue as I (for one) would no longer know if 'NA' is a valid or a missing data value.

For what it's worth, current idiomatic behaviour is that in a missing values would be replaced by `None`   `dtype`:
```python
   >>> array = [['No-one', 'Nadie'], ['Expects']]
   >>> df = pd.DataFrame(array, columns=['En', 'Es'])
           En     Es
   0   No-one  Nadie
   1  Expects   *None*
```

The `dtypes` here are:
```python
   >>> [type(i) for i in df['Es']]
   [<class 'str'>, <class 'NoneType'>]
```

Given this, my thought is that `NA` is not a suitable  *default* replacement for missing `str` `dtype` elements rather `None` of `NoneType` `dtype`