QST/DOC: (How) should downstream libraries use native/nullable dtypes?
### 

- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)


### Location of the documentation

e.g.
- https://pandas.pydata.org/docs/dev/user_guide/integer_na.html
- https://pandas.pydata.org/docs/dev/user_guide/boolean.html

### Documentation problem

The user guides for the native/nullable dtypes have prominent admonitions:

> IntegerArray is currently experimental. Its API or implementation may change without warning.
> BooleanArray is currently experimental. Its API or implementation may change without warning.

These are a little ambiguous from the perspective of a downstream library. Specifically, it's not clear whether the _existence_ of these dtypes is "experimental API", or if we can assume that they *will* exist in pandas (unless formally deprecated) but that nuances of their behavior may change.

For example, I am writing a function that needs to coerce a Series to boolean values in a missing-data friendly way. I'm reasonably happy delegating exactly what that means to pandas. Can I do `s.astype("boolean")` without assuming that it will break without warning? 

### Suggested fix for documentation

This is maybe more of a usage question, but I do think there's an actionable doc improvement. I understand wanting to iterate on the new ideas and that the admonition is written as broadly as possible to facilitate that. But these dtypes have been around for a while now, users are starting to expect that downstream libraries will support them, and it would be helpful to have a little bit more guidance about how to do that.