Setting CategoricalColumnNames is not Actually Doing Anything

**System Information (please complete the following information):**
- OS & Version: Windows 10
- ML.NET Version: Microsoft.ML, 2.0.1
Microsoft.ML.AutoML, 0.20.1
BUT ALSO ON LATEST PreRelease from dotnet-libraries
- .NET Version: 7.0

**Describe the bug**
ColumnInformation CategoricalColumnNames suppose to instruct automl to consider specific columns as categories which in turn should increase precision on the training.

In the past (couple of months ago) I did a test with setting columns to CategoricalColumnNames, it was some prerelease version from dotnet-libraries and the training result actually was better and training time was much longer.

Today, I have re-tested this and training time and score is exactly the same as not setting CategoricalColumnNames.

**To Reproduce**
download code (change extansion to ipynb) [ColumnInformationDoesNotWork.txt](https://github.com/dotnet/machinelearning/files/12335917/ColumnInformationDoesNotWork.txt)
open with vscode
download train.csv from [here](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/data?select=train.csv)
run ipynb
see scores are similiar for both with CI and without.

**Expected behavior**
When adding CI the training should handle the data differently and as well produce a better score with longer training time.

**Screenshots, Code, Sample Projects**
[ColumnInformationDoesNotWork.txt](https://github.com/dotnet/machinelearning/files/12335917/ColumnInformationDoesNotWork.txt)

**Additional context**
I might be missing something in parameter initialization of the process, if so please instruct me on what exactly to set.

**Code Snippets**
```
var set = new RegressionExperimentSettings();
set.MaxExperimentTimeInSeconds = 1; // Maxmodels bypass
set.Trainers.Clear();
set.Trainers.Add(RegressionTrainer.FastForest);
RegressionExperiment experiment = mlContext.Auto().CreateRegressionExperiment(set);
ColumnInformation CI = new ColumnInformation();
CI.CategoricalColumnNames.Add("family");
CI.CategoricalColumnNames.Add("store_nbr");
var x1 = experiment.Execute(train,CI);
var score1 = x1.BestRun.ValidationMetrics.RSquared;
Console.WriteLine("Result with categoricals definitions: " + score1);
```

```
var no = new RegressionExperimentSettings();
no.MaxExperimentTimeInSeconds = 1; // Maxmodels bypass
no.Trainers.Clear();
no.Trainers.Add(RegressionTrainer.FastForest);
CI.CategoricalColumnNames.Clear();
RegressionExperiment experiment2 = mlContext.Auto().CreateRegressionExperiment(no);
var x2 = experiment2.Execute(train,CI);
var score2 = x2.BestRun.ValidationMetrics.RSquared;
Console.WriteLine("Result without categoricals definitions: " + score2);
```

Thanks!