question: Understanding the relationship between `get_split_value_histogram` and `trees_to_dataframe`

Hi, thanks for the great library!

I am not sure if this is a bug or more of a misunderstanding on my part, but I am struggling to resolve some differences between the output of `get_split_value_histogram` and `trees_to_dataframe`. To my understanding, it should be possible to get the splits XGBoost uses for each feature from either method. However, I am getting drastically different results. As an example, here is a model with a float feature and some boolean features.

```
import numpy as np
import pandas as pd
import xgboost

N_ROWS = 1_000

np.random.seed(208)

X = pd.DataFrame({
# "bool": np.random.choice([True, False], N_ROWS),
"int_bool": np.random.choice([0, 1], N_ROWS),
"float_bool": np.random.choice([0.0, 1.0], N_ROWS),
"float": np.random.rand(N_ROWS),
})

y = np.random.choice([True, False], N_ROWS)

model = xgboost.XGBClassifier().fit(X, y)
booster = model.get_booster()
trees = booster.trees_to_dataframe()
```

Note that passing in a boolean column works, but breaks both `get_split_value_histogram` and `trees_to_dataframe` (already reported in https://github.com/dmlc/xgboost/issues/10437). Looking at "int_bool",

```
print(booster.get_split_value_histogram("int_bool"))
print(trees.loc[trees["Feature"] == "int_bool"]["Split"].value_counts())
```
I get outputs of

```
SplitValue Count
0 1.5 223.0
```

and

```
Split
1.0 175
```
respectively. According to `get_split_value_histogram`, there is a trivial split on 1.5, whereas `trees_to_dataframe` seems to report a more accurate split on 1.0. Where does the 1.5 come from?

Looking at the float feature,

```
print(booster.get_split_value_histogram("float").sort_values("SplitValue"))
print(
trees.loc[trees["Feature"] == "float"]
["Split"]
.value_counts()
.to_frame()
.reset_index()
.sort_values("Split")
)
```

I get

```
SplitValue Count
0 0.012732 8.0
1 0.017076 5.0
2 0.025765 7.0
3 0.030109 20.0
4 0.034454 4.0
.. ... ...
173 0.972841 9.0
174 0.977186 1.0
175 0.981530 5.0
176 0.990219 2.0
177 0.994563 11.0

[178 rows x 2 columns]
```

and

```
Split count
61 0.008877 7
20 0.015085 12
107 0.016895 5
17 0.021943 13
36 0.031006 10
.. ... ...
2 0.977809 22
5 0.983249 20
28 0.987834 11
30 0.992312 10
50 0.994810 8

[220 rows x 2 columns]
```
respectively. According to `get_split_value_histogram`, there are 178 unique splits, and according to `trees_to_dataframe`, there are 220 unique splits. In addition, the actual split values are fairly different between the two.

I would expect that the two functions return the same results.