### 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/test/java/org/elasticsearch/index/mapper/vectors/SparseVectorFieldMapperTests.java
);
        assertThat(ex.getCause().getMessage(), containsString("do not support dots in feature names"));
        assertThat(ex.getCause().getMessage(), containsString("politi.cs"));
    }
    public void testRejectMultiValuedFields() throws MapperParsingException, IOException {
        DocumentMapper mapper = createDocumentMapper(mapping(b -> {
            b.startObject("field").field("type", "sparse_vector").endObject();
            b.startObject("foo").startObject("properties");
            {
                b.startObject("field").field("type", "sparse_vector").endObject();
            }
            b.endObject().endObject();
        }));
        DocumentParsingException e = expectThrows(
            DocumentParsingException.class,
            () -> mapper.parse(source(b -> b.startObject("field").field("foo", Arrays.asList(10, 20)).endObject()))
        );
        assertEquals(
            "[sparse_vector] fields take hashes that map a feature to a strictly positive float, but got unexpected token " + "START_ARRAY",
            e.getCause().getMessage()
        );
        e = expectThrows(DocumentParsingException.class, () -> mapper.parse(source(b -> {
            b.startArray("foo");
            {
                b.startObject().startObject("field").field("bar", 10).endObject().endObject();
                b.startObject().startObject("field").field("bar", 20).endObject().endObject();
            }
            b.endArray();
        })));
        assertEquals(