from_dicts without strict=false can result in silent data loss with ragged data

[D3SL]

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

dataloss=pl.from_dicts({'colname': [{'intrange': {'prefix': '555',
    'middle': '111',
    'SN_Range': {'start': '00000000', 'stop': '99999999'}}},
  {'intrange': {'prefix': '555',
    'middle': '777',
    'SN_Range': {'start': '00000000', 'stop': '29999999'}},
   'testflag': 'true'}]})

noloss=pl.from_dicts({'colname': [{'intrange': {'prefix': '555',
    'middle': '111',
    'SN_Range': {'start': '00000000', 'stop': '99999999'}}},
  {'intrange': {'prefix': '555',
    'middle': '777',
    'SN_Range': {'start': '00000000', 'stop': '29999999'}},
   'testflag': 'true'}]},strict=False)

Log output

N/A no errors are raised. Jupyter gives no log output of any kind actually.

Issue description

When calling pl.from_dicts() on ragged data, very common with JSON, polars can silently drop data if strict is not set to false.

Expected behavior

strict should be irrelevant here. The issue is not column dtype but number of fields in each row of a struct column. Silent data loss should simply never happen.

Installed versions

--------Version info---------
Polars:              1.7.1
Index type:          UInt32
Platform:            Windows-10-10.0.19045-SP0
Python:              3.12.4 | packaged by conda-forge | (main, Jun 17 2024, 10:04:44) [MSC v.1940 64 bit (AMD64)]

----Optional dependencies----
adbc_driver_manager  <not installed>
altair               <not installed>
cloudpickle          <not installed>
connectorx           0.3.3
deltalake            <not installed>
fastexcel            <not installed>
fsspec               2024.6.1
gevent               <not installed>
great_tables         <not installed>
matplotlib           <not installed>
nest_asyncio         1.6.0
numpy                2.0.0
openpyxl             3.1.5
pandas               2.2.2
pyarrow              16.1.0
pydantic             <not installed>
pyiceberg            <not installed>
sqlalchemy           2.0.34
torch                <not installed>
xlsx2csv             <not installed>
xlsxwriter           3.2.0

#also tested on:
--------Version info---------
Polars:              1.8.1
Index type:          UInt32
Platform:            Linux-3.10.0-1160.119.1.el7.x86_64-x86_64-with-glibc2.36
Python:              3.11.8 (main, Mar 12 2024, 11:52:02) [GCC 12.2.0]
----Optional dependencies----
adbc_driver_manager  <not installed>
altair               <not installed>
cloudpickle          <not installed>
connectorx           <not installed>
deltalake            <not installed>
fastexcel            <not installed>
fsspec               <not installed>
gevent               <not installed>
great_tables         <not installed>
matplotlib           <not installed>
nest_asyncio         <not installed>
numpy                <not installed>
openpyxl             <not installed>
pandas               <not installed>
pyarrow              <not installed>
pydantic             <not installed>
pyiceberg            <not installed>
sqlalchemy           <not installed>
torch                <not installed>
xlsx2csv             <not installed>
xlsxwriter           <not installed>

[cmdlineluser]

It can also be seen directly from DataFrame:

pl.DataFrame({"a": [{"x": 1}, {"x": 1, "y": 2}]})
# shape: (2, 1)
# ┌───────────┐
# │ a         │
# │ ---       │
# │ struct[1] │
# ╞═══════════╡
# │ {1}       │
# │ {1}       │
# └───────────┘