### Apache Airflow Provider(s)

google

### Versions of Apache Airflow Providers

apache-airflow-providers-google==7.0.0

But the problem exists in all newer versions.

### Apache Airflow version

apache-airflow==2.3.2

### Operating System

Ubuntu 20.04.4 LTS

### Deployment

Official Apache Airflow Helm Chart

### Deployment details

_No response_

### What happened

The `list_rows` method in the BigQuery Hook does not return the page_token value, which is necessary for paginating query results. Same problem with `get_datasets_list` method. 

The documentation for the `get_datasets_list` method even states that the page_token parameter can be accessed:
```
            :param page_token:  Token representing a cursor into the datasets. If not passed,
            the API will return the first page of datasets. The token marks the beginning of the
            iterator to be returned and the value of the ``page_token`` can be accessed at
            ``next_page_token`` of the :class:`~google.api_core.page_iterator.HTTPIterator`.
```
but it doesn't return HTTPIterator. Instead, it converts the `HTTPIterator` to `list[DatasetListItem]` using `list(datasets)`, making it impossible to retrieve the original `HTTPIterator` and thus impossible to obtain the `next_page_token`.

### What you think should happen instead

`list_rows` \ `get_datasets_list` methods should return `Iterator` OR both the list of rows\datasets and the page_token value to allow users to retrieve multiple results pages. For backward compatibility, we can have a parameter like `return_iterator=True` or smth like that. 

### How to reproduce

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [X] 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)

must be provided. If a field is listed in ``fields`` and is ``None`` in
        ``dataset``, it will be deleted.
        If ``dataset.etag`` is not ``None``, the update will only
        succeed if the dataset on the server has the same ETag. Thus
        reading a dataset with ``get_dataset``, changing its fields,
        and then passing it to ``update_dataset`` will ensure that the changes
        will only be saved if no modifications to the dataset occurred
        since the read.
        :param dataset_resource: Dataset resource that will be provided
            in request body.
            https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource
        :param dataset_id: The id of the dataset.
        :param fields: The properties of ``dataset`` to change (e.g. "friendly_name").
        :param project_id: The Google Cloud Project ID
        :param retry: How to retry the RPC.
        """
        dataset_resource["datasetReference"] = dataset_resource.get("datasetReference", {})
        for key, value in zip(["datasetId", "projectId"], [dataset_id, project_id]):
            spec_value = dataset_resource["datasetReference"].get(key)
            if value and not spec_value:
                dataset_resource["datasetReference"][key] = value
        self.log.info("Start updating dataset")
        dataset = self.get_client(project_id=project_id).update_dataset(
            dataset=Dataset.from_api_repr(dataset_resource),
            fields=fields,
            retry=retry,
        )
        self.log.info("Dataset successfully updated: %s", dataset)
        return dataset
    def patch_dataset(self, dataset_id: str, dataset_resource: dict, project_id: str | None = None) -> dict: