CSV schema inference assumes Utf8 for empty columns

[kskalski]

Describe the bug
When rows processed by schema inference do not contain any data (are empty) for given column, that column is inferred as nullable DataType::Utf8. This data type is in fact a "catch-all" that permits any values later on, but it is in fact a limiting and to a degree incorrect behavior, since user is led to assume this column did contain some data and it was string or something that forced string type.

To Reproduce

int_column,null_column,string_column
1,,"a"
2,,"b"

Expected behavior
Inference should return int*, null, utf8

Additional context
My algorithm uses inference with limited number of rows as a kind of best-effort / incremental performance improvement, when I read some data and see inferred schema has nulls, I may repeat inference with more rows or without row limit. If inference wrongly returns some data-type that isn't there, then I will end up with unnecessarily widened Utf8 datatime, while in fact later on this column actually contains just ints or booleans.

Another use-case is that I have several files of the same shape (or they could be several random offsets into the same file) and I want to infer schemas for each of them, then merge them to see if any still contain nulls.
With #4901 and fixing behavior described in this issue I can implement above strategy correctly.

[tustvold]

This should be a relatively straightforward change to the logic in InferredDataType::get to return Null if self.packed == 0

[tustvold]

label_issue.py automatically added labels {'arrow'} from #4910