[FEA] Recognize pandas_categorical field in LightGBM model

Reproduce
```
X = (
pd.DataFrame(
np.array(30 * [["a", 1]] + 30 * [["b", 2]] + 30 * [["c", 3]]),
columns=["x1", "x2"],
)
.assign(x1=lambda df: df["x1"].astype("category"))
.assign(x2=lambda df: df["x2"].astype(float))
)
y = np.array(60 * [5] + 30 * [10])

X.dtypes

bst = lgb.LGBMRegressor(n_estimators=5)
bst.fit(X, y)

bst.predict(X.head(2))
bst.booster_.save_model("model.txt")

model = treelite.Model.load('model.txt', model_format='lightgbm')
model.export_lib(toolchain="gcc", libpath='./mymodel.so', verbose=True)
predictor = treelite_runtime.Predictor('./mymodel.so', verbose=True)
```
the above all works, but not sure how to use `predictor`, as

```
dmat = treelite_runtime.DMatrix(X.head(2).to_numpy())
predictor.predict(dmat)
```

leads to error like

```
41 return 'float64'
42 else:
---> 43 raise ValueError(f'Unrecognized NumPy type: {type_info}')
44
45

ValueError: Unrecognized NumPy type: object
```

The model file is like
[model.txt](https://github.com/dmlc/treelite/files/7910097/model.txt)

at the end of `model.txt`, it has
```
...

end of parameters

pandas_categorical:[["a", "b", "c"]]
```

My guess is that treelite doesn't make use of the `pandas_categorical` line yet, so trying to confirm.

Versions:

```
lightgbm==3.1.1
treelite==2.2.0
treelite-runtime==2.2.0
```