qrandint/qlograndint's documented inclusive upper bound is never sampled

### Describe the bug

`qrandint`/`qlograndint` document "lower is inclusive, upper is also inclusive (!)", but the upper bound is unreachable in the common case. `Quantized.sample()` has a q=1 fast path that skips quantization and calls the wrapped `Integer` sampler directly, which draws exclusive of `domain.upper` (same contract as plain `randint`). Since `qrandint`'s default is `q=1`, the documented-inclusive upper is silently never sampled.

This isn't just cosmetic: `flaml/automl/time_series/ts_model.py`'s built-in ARIMA and SARIMAX estimators define their p/d/q/P/D/Q search spaces with `tune.qrandint(lower=0, upper=X, q=1)` at 9 call sites, so the shipped time-series AutoML search never explores the top value of any of those hyperparameters.

For q>1 it's also unreliable, just less consistently: the general branch rounds a raw exclusive-upper draw, so whether the nominal upper bound is reached depends on the rounding fraction.

### Steps to reproduce

```python
from flaml.tune.sample import qrandint
import numpy as np

rs = np.random.RandomState(0)
vals = {qrandint(1, 10).sample(spec=None, random_state=rs) for _ in range(20000)}
print(max(vals)) # 9, never 10

rs = np.random.RandomState(0)
vals = {qrandint(0, 10, 2).sample(spec=None, random_state=rs) for _ in range(20000)}
print(max(vals)) # 8, never 10
```

### Expected Behavior

`qrandint(1, 10)` should sample 10 at least occasionally, matching its own docstring.

### Additional Information

FLAML version: current main (f9e087c). I have a fix ready (extends the sampled range by one quantization step for `Integer` domains, then clamps) and I'll open a PR against this issue.

Start in flaml.tune.sample with Quantized.sample() and reproduce the qrandint examples using the shown NumPy RandomState. Check the documented inclusive upper bound for q=1 and q>1, including the time-series call sites in flaml/automl/time_series/ts_model.py. Done means the upper value can be sampled and regression tests cover both cases.

Written by the indexing model from the issue text.