[PERF] Improve performace of read_csv with memory_map=True when file encoding is UTF-8
- [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry

This PR improves performance of read_csv with memory_map=True and a UTF-8-encoded file by eliminating unnecessary `decode()` call. The code below demonstrates the speed improvement:
```
import timeit
import pandas as pd

def perftest_readcsv_memmap_utf8():
    lines = []
    for lnum in range(0x20, 0x10080, 0x80):
        line = "".join([chr(c) for c in range(lnum, lnum + 0x80)]) + "\n"
        try:
            line.encode("utf-8")
        except UnicodeEncodeError:
            continue
        lines.append(line)
    df = pd.DataFrame(lines)
    df = pd.concat([df for n in range(1000)], ignore_index=True)
    fname = "test_readcsv_utf8.csv"
    df.to_csv(fname, index=False, header=False, encoding="utf-8")
    ti_rep = 5
    ti_num = 10
    time_dfmem = timeit.repeat(f'dfnomem = pd.read_csv("{fname}", header=None, memory_map=True, engine="c")',
                  setup='import pandas as pd',
                  repeat=ti_rep, number=ti_num)
    print(f"Read CSV (memory_map=True), repeat={ti_rep}: {time_dfmem}")
    print(f"Median: {pd.Series(time_dfmem).median()}")

perftest_readcsv_memmap_utf8()
```
On my machine the results are:
Without patch:
```
Read CSV (memory_map=True), repeat=5: [8.107480439008214, 8.100845866953023, 8.135483622085303, 8.090781628969125, 8.068992758984677]
Median: 8.100845866953023
```
With patch:
```
Read CSV (memory_map=True), repeat=5: [5.280414769076742, 5.290980814024806, 5.127453051973134, 5.150275847059675, 5.276113986037672]
Median: 5.276113986037672
```
The improved code runs in ~65% of time of the current code.
The spedup depends on the contents of the file; the test code above creates a 182 MB file containing almost all of Unicode Plane 0 and a several of Plane 1 characters UTF-8-encoded in 1, 2, 3 and 4 bytes; in this respect, it is a worst case. I also tested this patch on my 8.8 GB CSV file consisting of 1 and 2-byte encoded characters and the code ran in ~75% of time of the current code.
