### 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)

break
                    if dag.end_date and dt > dag.end_date:
                        break
                    dates[dt.date()] += 1
            else:
                prev_logical_date = DateTime.min
                while True:
                    curr_info = dag.timetable.next_dagrun_info(
                        last_automated_data_interval=last_automated_data_interval,
                        restriction=restriction,
                    )
                    if curr_info is None:
                        break  
                    if curr_info.logical_date <= prev_logical_date:
                        break  
                    if curr_info.logical_date.year != year:
                        break  
                    last_automated_data_interval = curr_info.data_interval
                    dates[curr_info.logical_date] += 1
                    prev_logical_date = curr_info.logical_date
            data_dag_states.extend(
                {"date": date.isoformat(), "state": "planned", "count": count}
                for (date, count) in dates.items()
            )
        now = DateTime.utcnow()
        data = {
            "dag_states": data_dag_states,
            "start_date": (dag.start_date or DateTime.utcnow()).date().isoformat(),
            "end_date": (dag.end_date or now).date().isoformat(),
        }