Can we take advantage of the Vector API for text analysis?

### Description

`StandardTokenizer` is likely our most widely used tokenizer, and is reported as the main bottleneck for indexing in our nightly benchmarks, see e.g. top 5 CPU users for the 1kB Wikipedia corpus on yesterday's run:

```
PERCENT CPU SAMPLES STACK
10.54% 51017 org.apache.lucene.analysis.standard.StandardTokenizerImpl#getNextToken()
6.99% 33852 org.apache.lucene.index.IndexingChain$PerField#invertTokenStream()
6.47% 31309 org.apache.lucene.index.TermsHashPerField#writeByte()
5.00% 24183 org.apache.lucene.util.BytesRefHash#equals()
4.38% 21215 java.lang.Character#codePointAtImpl()
```

Intuitively, this kind of workload is amenable to vectorization, could we take advantage of vectorization to speed up text analysis and thus indexing?

Start with StandardTokenizer and StandardTokenizerImpl#getNextToken, then inspect the nightly benchmark data for the 1kB Wikipedia corpus to understand where vectorization might apply. Done would require demonstrating a meaningful text-analysis or indexing speedup without changing tokenizer behavior.

Written by the indexing model from the issue text.