### Apache Airflow version

2.4.1

### What happened

When using dataset scheduling, it isn't obvious which datasets a downstream dataset consumer is awaiting in order for the DAG to be scheduled.

I would assume that this is supposed to be solved by the `Latest Update` column in the modal that opens when selecting `x of y datasets updated`, but it appears that the data isn't being populated.

<img width="601" alt="image" src="https://user-images.githubusercontent.com/5778047/194116186-d582cede-c778-47f7-8341-fc13a69a2358.png">

Although one of the datasets has been produced, there is no data in the `Latest Update` column of the modal.

In the above example, both datasets have been produced > 1 time.

<img width="581" alt="image" src="https://user-images.githubusercontent.com/5778047/194116368-ceff241f-a623-4893-beb7-637b821c4b53.png">

<img width="581" alt="image" src="https://user-images.githubusercontent.com/5778047/194116410-19045f5a-8400-47b0-afcb-9fbbffca26ee.png">


### What you think should happen instead

The `Latest Update` column should be populated with the latest update timestamp for each dataset required to schedule a downstream, dataset consuming DAG.

Ideally there would be some form of highlighting on the "missing" datasets for quick visual feedback when DAGs have a large number of datasets required for scheduling.

### How to reproduce

1. Create a DAG (or 2 individual DAGs) that produces 2 datasets
2. Produce both datasets
3. Then produce _only one_ dataset
4. Check the modal by clicking from the home screen on the `x of y datasets updated` button.

### Operating System

Debian GNU/Linux 11 (bullseye)

### Versions of Apache Airflow Providers

_No response_

### Deployment

Docker-Compose

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

dag_runs = wwwutils.sorted_dag_runs(query, ordering=dag.timetable.run_ordering, limit=num_runs)
            encoded_runs = [wwwutils.encode_dag_run(dr) for dr in dag_runs]
            data = {
                "groups": dag_to_grid(dag, dag_runs, session),
                "dag_runs": encoded_runs,
                "ordering": dag.timetable.run_ordering,
            }
        
        return (
            htmlsafe_json_dumps(data, separators=(",", ":"), dumps=flask.json.dumps),
            {"Content-Type": "application/json; charset=utf-8"},
        )
    @expose("/object/next_run_datasets/<string:dag_id>")
    @auth.has_access([(permissions.ACTION_CAN_READ, permissions.RESOURCE_DAG)])
    def next_run_datasets(self, dag_id):
        """Returns datasets necessary, and their status, for the next dag run"""
        dag = get_airflow_app().dag_bag.get_dag(dag_id)
        if not dag:
            return {"error": f"can't find dag {dag_id}"}, 404
        with create_session() as session:
            data = [
                dict(info)
                for info in session.query(
                    DatasetModel.id,
                    DatasetModel.uri,
                    func.max(DatasetEvent.timestamp).label("lastUpdate"),
                )
                .join(DagScheduleDatasetReference, DagScheduleDatasetReference.dataset_id == DatasetModel.id)
                .join(
                    DatasetDagRunQueue,