[BUG] Benchmark random data generator produces skewed distributions and invalid floating-point values

**Describe the bug**

While working on #18290, I discovered that libcudf benchmarks fail to reproduce the expected performance improvements for very low-cardinality groupby cases observed by Devtech. Upon further investigation, it became evident that the random data generator used in benchmarks is not functioning correctly.

To verify this, I ran a small test by generating a 100-element `int32_t` column while varying the cardinality from 1 to 32. The output from `cudf::test::print` revealed a clear issue:
```
[cardinality=1]
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
[cardinality=2]
0,0,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,0,0,8,8,8,8,8,0,0,0,0,0,0,0,8,8,8,8,8,8,0,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8
[cardinality=4]
0,0,60,60,60,60,60,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,0,0,60,60,60,60,60,8,8,8,8,8,8,8,60,60,60,60,60,60,0,60,60,60,60,60,60,60,60,60,60,90,90,90,90,90,90,90,60,60,60,60,60,60,60,60,60,60,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,60,60,60,60,60,60,90,90,90,90,90,90,90
[cardinality=8]
0,0,97,97,97,97,97,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,8,8,97,97,97,97,97,90,90,90,90,60,60,60,19,19,19,19,19,19,0,97,97,97,97,97,97,97,97,97,97,52,52,52,52,52,52,52,97,97,97,97,97,97,97,97,97,97,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,19,19,19,19,19,19,40,40,40,40,40,40,40
[cardinality=16]
0,8,75,75,75,75,75,59,59,59,59,59,59,59,59,51,51,51,51,51,51,51,51,90,90,26,26,26,26,26,52,52,52,52,97,97,97,56,56,56,56,56,56,8,26,26,26,26,26,75,75,75,75,75,58,58,58,58,58,58,58,75,75,75,75,75,26,26,26,26,26,59,59,59,59,59,59,59,59,51,51,51,51,51,51,51,51,56,56,56,56,56,56,51,51,51,51,51,51,51
[cardinality=32]
0,60,97,97,97,97,97,91,91,91,91,91,91,91,91,94,94,94,94,94,94,94,94,52,52,88,88,88,88,88,58,58,58,58,26,26,26,65,65,65,65,65,65,60,100,100,100,100,100,73,73,73,73,73,97,97,97,97,97,97,97,73,73,73,73,73,88,88,88,88,88,91,91,91,91,91,91,91,91,41,41,41,41,41,41,41,41,65,65,65,65,65,65,94,94,94,94,94,94,94
```

- When cardinality = 1, the results are as expected, with all values being the same.
- However, when cardinality = 2, the distribution is highly unbalanced, e.g. 0 appears **13 times**, while 8 appears **83 times** instead of being evenly split (~50 each).
- This pattern of skewed distributions persists across other cardinalities, as demonstrated in the test results.

Such an imbalance significantly impacts benchmarking accuracy. Ideally, a uniform distribution would ensure that values are evenly represented, but the flawed data generator introduces biases that distort performance measurements. **This affects almost all of our cardinality benchmarks.**

Another issue lies with the floating-point generator. When generating 100 `double` elements within the range [0, 100], all values ended up being very close to either the max or the min value of `double`. This clearly indicates a serious problem with the generator, as the output is nowhere near the expected range.
```
-8.9884656743115785e+307,-1.900795218050189e+307,5.8237058299790331e+306,5.8237058299790331e+306,
5.8237058299790331e+306,5.8237058299790331e+306,5.8237058299790331e+306,-1.9738458611542523e+307,
-1.9738458611542523e+307,-1.9738458611542523e+307,-1.9738458611542523e+307,-1.9738458611542523e+307,
-1.9738458611542523e+307,-1.9738458611542523e+307,-1.9738458611542523e+307,-7.8726545643607571e+306,
-7.8726545643607571e+306,-7.8726545643607571e+306,-7.8726545643607571e+306,-7.8726545643607571e+306,
-7.8726545643607571e+306,-7.8726545643607571e+306,-7.8726545643607571e+306,1.8018745534686453e+307,
1.8018745534686453e+307,-2.727274834075473e+307,-2.727274834075473e+307,-2.727274834075473e+307,
-2.727274834075473e+307,-2.727274834075473e+307,-3.9511159250950192e+307,-3.9511159250950192e+307,
-3.9511159250950192e+307,-3.9511159250950192e+307,-2.9220632163786983e+307,-2.9220632163786983e+307,
-2.9220632163786983e+307,-3.9550326807426145e+307,-3.9550326807426145e+307,-3.9550326807426145e+307,
-3.9550326807426145e+307,-3.9550326807426145e+307,-3.9550326807426145e+307,-1.900795218050189e+307,
-4.6111471708172531e+306,-4.6111471708172531e+306,-4.6111471708172531e+306,-4.6111471708172531e+306,
-4.6111471708172531e+306,3.996709520468354e+307,3.996709520468354e+307,3.996709520468354e+307,
3.996709520468354e+307,3.996709520468354e+307,5.1955152235197585e+307,5.1955152235197585e+307,
5.1955152235197585e+307,5.1955152235197585e+307,5.1955152235197585e+307,5.1955152235197585e+307,
5.1955152235197585e+307,7.2599567509417792e+307,7.2599567509417792e+307,7.2599567509417792e+307,
7.2599567509417792e+307,7.2599567509417792e+307,-2.727274834075473e+307,-2.727274834075473e+307,
-2.727274834075473e+307,-2.727274834075473e+307,-2.727274834075473e+307,6.640906131316438e+306,
6.640906131316438e+306,6.640906131316438e+306,6.640906131316438e+306,6.640906131316438e+306,
6.640906131316438e+306,6.640906131316438e+306,6.640906131316438e+306,1.0145923254390491e+307,
1.0145923254390491e+307,1.0145923254390491e+307,1.0145923254390491e+307,1.0145923254390491e+307,
1.0145923254390491e+307,1.0145923254390491e+307,1.0145923254390491e+307,5.9736272051675239e+306,
5.9736272051675239e+306,5.9736272051675239e+306,5.9736272051675239e+306,5.9736272051675239e+306,
5.9736272051675239e+306,-7.8726545643607571e+306,-7.8726545643607571e+306,-7.8726545643607571e+306,
-7.8726545643607571e+306,-7.8726545643607571e+306,-7.8726545643607571e+306,-7.8726545643607571e+306
```

**Steps/Code to reproduce bug**
```cpp
auto constexper num_rows = 100;
auto constexper cardinality = 1; // [2, 4, 8, 16, 32]

auto const keys = [&] {
data_profile const profile =
data_profile_builder()
.cardinality(cardinality)
.no_validity()
.distribution(cudf::type_to_id(), distribution_id::UNIFORM, 0, num_rows);
return create_random_column(cudf::type_to_id(), row_count{num_rows}, profile);
}();
auto const vals = [&] {
auto builder = data_profile_builder().cardinality(0).no_validity().distribution(
cudf::type_to_id(), distribution_id::UNIFORM, 0, num_rows);
return create_random_column(
cudf::type_to_id(), row_count{num_rows}, data_profile{builder});
}();

cudf::test::print(keys->view());
cudf::test::print(vals->view());

```

**Expected behavior**

1. With normal distribution, distinct elements should be evenly distributed across the range without obvious skew.
2. Floating-point elements should be generated within the specified range.