Adding missing columns with a string default

[ymiftah]

Describe the bug
When defining a schema with a default value of string type, and enforcing auto-addition of the columns (add_missing_columns=True), the validation fails.

• I have checked that this issue has not already been reported.
• I have confirmed this bug exists on the latest version of pandera.
• (optional) I have confirmed this bug exists on the main branch of pandera.

Code Sample, a copy-pastable example

# Your code here
import polars as pl
import pandera.polars as pa

schema = pa.DataFrameSchema(
    columns={
        "a": pa.Column(int),
        "b": pa.Column(float, default=1),
        "c": pa.Column(str, default="foo"),
        "d": pa.Column(float, nullable=True),
    },
    add_missing_columns=True,
    coerce=True,
)
df = pl.DataFrame({"a": [1, 2, 3]})
schema.validate(df)

The above throws the following error

ColumnNotFoundError: foo

This error occurred with the following context stack:
	[1] 'with_columns' failed
	[2] 'with_columns' input failed to resolve
	[3] 'select' input failed to resolve
	[4] 'with_columns' input failed to resolve

Expected behavior

The column "c" should be defaulted to "foo" after validation.

Desktop (please complete the following information):

• OS: Ubuntu
• Browser: NA
• Version: NA

name = "polars"
version = "0.20.30"

name = "pandera"
version = "0.19.3"