### Description

When using a ML model with that outputs sparse vectors (like ELSER), it's possible that some times the inference pipeline fails when indexing new documents and new ingested documents are not enriched with the `sparse_vector` fields.

One approach in this case is to run an update by query and issue a reindexing with the ml pipeline of the documents in place in the same index.
In this case, we would want to only update the documents that do not have `sparse_vector` fields, for example:

```
POST search-books/_update_by_query?pipeline=ml-inference-books
{
  "query": {
    "bool": {
      "must_not": {
        "exists": {
          "field": "ml.inference.description_expanded.predicted_value"
        }
      }
    }
  }
}
```
In theory this a much simpler approach then duplicating all the data to reindex in another index or to reissue bulk indexing requests from scratch. It only requires a single Elasticsearch API request.

the problem is that `sparse_vector` fields do not support `exists` queries.

```
{
  "error": {
    "root_cause": [
      {
        "type": "query_shard_exception",
        "reason": "failed to create query: [sparse_vector] fields do not support [exists] queries",
        "index_uuid": "WkxxefxdTuCiNeLy4b33jw",
        "index": "search-books"
      }
    ],
    "type": "search_phase_execution_exception",
    "reason": "all shards failed",
    "phase": "query",
    "grouped": true,
    "failed_shards": [
      {
        "shard": 0,
        "index": "search-books",
        "node": "l5lxDFkeSwOyiwOEkQtWZA",
        "reason": {
          "type": "query_shard_exception",
          "reason": "failed to create query: [sparse_vector] fields do not support [exists] queries",
          "index_uuid": "WkxxefxdTuCiNeLy4b33jw",
          "index": "search-books",
          "caused_by": {
            "type": "illegal_argument_exception",
            "reason": "[sparse_vector] fields do not support [exists] queries"
          }
        }
      }
    ]
  },
  "status": 400
}
```

This also makes it difficult to get an accurate count of how many fields are missing the `sparse_vector` fields and users have to rely on other fields/mechanisms to do a reindexing in place with update by query.

NOTE: This will only be for the top level sparse_vector field. NOT for any of its sparse dimensions.

server/src/internalClusterTest/java/org/elasticsearch/search/query/ExistsIT.java
expected.put("bar.bar", 1);
        expected.put("bar.bar.bar", 1);
        expected.put("foobar", 0);
        final long numDocs = sources.length;
        SearchResponse allDocs = client().prepareSearch("idx").setSize(sources.length).get();
        assertSearchResponse(allDocs);
        assertHitCount(allDocs, numDocs);
        for (Map.Entry<String, Integer> entry : expected.entrySet()) {
            final String fieldName = entry.getKey();
            final int count = entry.getValue();
            
            SearchResponse resp = client().prepareSearch("idx").setQuery(QueryBuilders.existsQuery(fieldName)).get();
            assertSearchResponse(resp);
            try {
                assertEquals(
                    String.format(
                        Locale.ROOT,
                        "exists(%s, %d) mapping: %s response: %s",
                        fieldName,
                        count,
                        Strings.toString(mapping),
                        resp
                    ),
                    count,
                    resp.getHits().getTotalHits().value
                );
            } catch (AssertionError e) {
                for (SearchHit searchHit : allDocs.getHits()) {
                    final String index = searchHit.getIndex();
                    final String id = searchHit.getId();