TST(string dtype): Resolve xfails in test_from_dummies

[rhshadrach]

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The current behavior assumes the default_category provided can be coerced to the dtype of the input's columns. When the input's columns labels are strings, and the default_category is an integer, currently with object dtype we end up with values that are a mix of strings and integers. With infer_string=True where the input's columns are str dtype, we end up instead with all strings (coercing the integer to a string).

It's not clear to me whether this case should result in object dtype with a mix of strings and integers, or str dtype. Thoughts here are welcome. A few cases to consider are below. Currently I'm going with backwards compatibility, but open to other directions.

df = DataFrame({3: [1, 0, 0], 4: [0, 1, 0]})
result = pd.from_dummies(df, default_category=0.5)
# ValueError: Trying to coerce float values to integers

df = DataFrame({"x": [1, 0, 0], "y": [0, 1, 0]})
result = pd.from_dummies(df, default_category=5)
print(type(result.iloc[2, 0]))
# <class 'int'>