collect_schema produces incorrect results for an aggregation function followed by .dt.*. in a group_by context.

[christian-hnz]

Checks

• I have checked that this issue has not already been reported.
• I have confirmed this bug exists on the latest version of Polars.

Reproducible example

from datetime import date
import polars as pl

df = pl.DataFrame(
    {
        "id": ["a", "a", "a", "b", "b"],
        "date": [date(2000, i, 1) for i in (1, 4, 7, 6, 12)],
    }
)
min_date = pl.col("date").min()

df.group_by("id").agg(min_date.dt.day()).schema  # Schema({'id': String, 'literal': Int8}) 
df.lazy().group_by("id").agg(min_date.dt.day()).collect_schema()  # Schema({'id': String, 'literal': List(Int8)})

Log output

keys/aggregates are not partitionable: running default HASH AGGREGATION

Issue description

Methods from the .dt namespace lead to an incorrect conclusion by collect_schema when used in a group_by context (it works correctly when replacing the above .group_by("id").agg( by .select() and when preceded by an aggregation function.

• The below example shows min but it behaves the same way with max, mean, median, ...
• The example yields then same results when replacing day by month or year.

Expected behavior

Calling collect_schema on the LazyFrame should also yield Schema({'id': String, 'literal': Int8}) instead of Schema({'id': String, 'literal': List(Int8)}).

Installed versions

--------Version info---------
Polars:              1.15.0
Index type:          UInt32
Platform:            Linux-6.8.0-109048-tuxedo-x86_64-with-glibc2.39
Python:              3.12.7 (main, Oct 20 2024, 10:55:11) [GCC 13.2.0]
LTS CPU:             False

----Optional dependencies----
adbc_driver_manager  <not installed>
altair               <not installed>
boto3                <not installed>
cloudpickle          3.1.0
connectorx           <not installed>
deltalake            <not installed>
fastexcel            <not installed>
fsspec               2024.10.0
gevent               <not installed>
google.auth          <not installed>
great_tables         <not installed>
matplotlib           3.9.2
nest_asyncio         <not installed>
numpy                2.0.2
openpyxl             <not installed>
pandas               2.2.3
pyarrow              18.0.0
pydantic             <not installed>
pyiceberg            <not installed>
sqlalchemy           <not installed>
torch                <not installed>
xlsx2csv             <not installed>
xlsxwriter           <not installed>

[lmocsi]

This seems to have the same root cause as my issue: #19984

[christian-hnz]

Wow, that was fast. Thanks for fixing!