feat: Raise informative error message if non-IntoExpr is passed by name in *Frame.group_by

[MarcoGorelli]

closes #17540

Although the current behaviour is technically correct, I think this is worth doing - the pandas docs have examples such as

df.groupby(by=["b"], dropna=False).sum()

so the many pandas users trying out Polars would now get an informative error

I did debate whether this should raise a warning instead of an error, but my

---

Demo:

import polars as pl

df = pl.DataFrame({"a": [1, 1, 2], "b": [4, 5, 6]})

print(df.group_by(by=["a"]).agg([pl.col("b").sum()]))

TypeError: Expected Polars expression or object convertible to one, got <class 'list'>.

Hint: if you tried
    group_by(by=['a'])
then you probably want to use this instead:
    group_by(['a'])

I did debate whether it would be better to raise a warning instead...however, I really can't think of a use case for passing a list literal to group_by, it defeats the purpose of the operation

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

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##             main   #17654   +/-   ##
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  Coverage   80.70%   80.71%           
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  Lines      195511   195535   +24     
  Branches     2782     2786    +4     
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[tylerriccio33]

@MarcoGorelli I was looking into this issue last night and thought the GroupBy class could have it's type hints switched to something other than IntoExpr. The IntoExpr includes List[Any] (part of PythonLiteral), which is no longer allowed. Should there be a new type like GroupByIntoExpr?

The type could look something like this (below) which more accurately represents what input should go into the keyword arguments.

GroupByIntoExpr: TypeAlias = Union[NonNestedLiteral, IntoExprColumn, None]