I get a "RepresenterError" when trying to log a model using service principal

## Which example? Describe the issue

For the below code based on this sample notebook;
https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/train-with-mlflow/xgboost_service_principal.ipynb

I get a "RepresenterError" when trying to log a model using service principal, and not with user. SP is an appregistration and has the role "AzureML Data Scientist" on the aml workspace. Logging metrics with SP will not produce errors, but logging model will. Have tested with mlflow 1.27.0 and 1.28.0. Is this expected behaviour or bug? The mlflow.sklearn.autolog() as done in sample notebook does not work either.

example:
```python
"name": "RepresenterError",
"message": "('cannot represent an object', OrderedDict([('name', 'mlflow-env'), ('channels', ['conda-forge']), ('dependencies', ['python=3.9.7', 'pip<=21.2.4', {'pip': ['mlflow', 'cloudpickle==2.1.0', 'psutil==5.9.1', 'scikit-learn==1.1.2', 'typing-extensions==4.3.0']}])]))", .... and so on ....
```

## Additional context

This code will generate the error:

```python
#%%
import os
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential
import mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
import pandas as pd
from azureml.core import Workspace

### SECTION A: Create credentials from service principal will produce error
os.environ['AZURE_TENANT_ID']="..."
os.environ['AZURE_CLIENT_ID']="..."
os.environ['AZURE_CLIENT_SECRET']="..."
credentials = DefaultAzureCredential()
ml_client = MLClient.from_config(credential=credentials)
ws = ml_client.workspaces.get(name="wsname")
mlflow.set_tracking_uri(ws.mlflow_tracking_uri)
### END A

### SECTION B: Alternative, use credentials for user which will not produce error (Replace A with B)
#ws = Workspace.from_config('config.json')
#ws.get_mlflow_tracking_uri()
#mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())
### END B

mlflow.set_experiment("expname")

# Fit a simple model and log it
data_uri = "https://azuremlexamples.blob.core.windows.net/datasets/iris.csv"
df = pd.read_csv(data_uri)

X = df.drop(["species"], axis=1)
y = df["species"]
enc = LabelEncoder()
y = enc.fit_transform(y)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier()
model.fit(X_train, y_train)

# log model
with mlflow.start_run() as run:
mlflow.sklearn.log_model(model, 'modelpath')
```
-

Start with the xgboost_service_principal.ipynb sample and run the provided RandomForestClassifier reproducer using both DefaultAzureCredential and Workspace credentials. Compare the tracking and model-logging paths, including the stated MLflow versions, and determine why service-principal authentication produces RepresenterError. Done means model logging, and the notebook's autolog path, work consistently with the service principal or the limitation is documented.

Written by the indexing model from the issue text.