Add integration test for `dss create` to check MLflow access

### Why it needs to get done

This is needed in order to ensure that created pods can access MLflow. This was initially [suggested here](https://github.com/canonical/data-science-stack/pull/43#discussion_r1558991266) by @misohu in the `dss create` PR.

### What needs to get done

Write an integration test which will create a notebook and then it will run the script to create experiement in MLflow. e.g.

```
import mlflow
c = mlflow.MlflowClient() # You should not need to specify the tracking_uri - it should be populated automatically
print(c.tracking_uri) # should print the in-cluster url for dss's mlflow
c.create_experiment("test-experiment") # Should succeed
```

The idea is to create notebook and then run kubectl exec with that script. Or we could also execute a jupyter notebook from inside the pod (which could be `git clone` and run).

Something [like this](https://github.com/canonical/kfp-operators/blob/6be2104216bafd2e26f68ce967f8be04af781781/self-hosted/pod.yaml#L12-L23) (from [PR](https://github.com/canonical/kfp-operators/pull/415/files)) could also be used to run command from the pod.

### When is the task considered done

There is integration test checking MLflow access.