lib: optimize TextEncoder.encode for short strings - #66831

(.venv) ➜  node git:(mert/textencoder-small-string) ✗ npx node-benchmark-compare te-clean.csv                                                        
                                                                          confidence improvement accuracy (*)   (**)  (***)
util/text-encoder.js op='encode' type='ascii' n=1000000 len=256                    *      1.67 %       ±1.45% ±1.94% ±2.54%
util/text-encoder.js op='encode' type='ascii' n=1000000 len=32                   ***    127.18 %       ±1.93% ±2.58% ±3.40%
util/text-encoder.js op='encode' type='one-byte-string' n=1000000 len=256                 1.39 %       ±2.07% ±2.79% ±3.68%
util/text-encoder.js op='encode' type='one-byte-string' n=1000000 len=32         ***     85.44 %       ±2.43% ±3.27% ±4.31%
util/text-encoder.js op='encode' type='two-byte-string' n=1000000 len=256                -0.12 %       ±0.86% ±1.15% ±1.49%
util/text-encoder.js op='encode' type='two-byte-string' n=1000000 len=32         ***     74.83 %       ±1.45% ±1.94% ±2.54%

Be aware that when doing many comparisons the risk of a false-positive result increases.
In this case, there are 6 comparisons, you can thus expect the following amount of false-positive results:
  0.30 false positives, when considering a   5% risk acceptance (*, **, ***),
  0.06 false positives, when considering a   1% risk acceptance (**, ***),
  0.01 false positives, when considering a 0.1% risk acceptance (***)