Why are there so many sorting algorithms?

Because different sorting algorithms are useful in different circumstances.
Some (Bubble sort) are simple to implement, and thus useful for teaching.
Some (Merge sort, Heap sort) have a relatively low limit on the worst-case number of operations they have to perform. If they need to sort N elements, they will never need more than O(N log N). Note: see here for a definition of O()
Some (Insertion sort, Cube sort) have a low number of "best case" operations, meaning that they finish quickly if the list of already sorted. For N elements, their best case will be O(N)
Some (Heap sort) need very little memory
Some (Bead sort) only work with positive integers
Some... well, you get the point.
An engineer that wants to use a sorting algorithm would do well to think about the characteristics of the data that needs to be sorted, the constraints of processor, the availability of parallel processors, and many other constraints. While some algorithms are more popular than others, there is no single algorithm that is best in all circumstances.
The same is true for quantum algorithms.
There is no single implementation of state preparation that's always best. Some use fewer qubits. Some are more accurate. Some have smaller depth. So the choice of the best state preparation depends on the system you are using but also on what else is going on: what else are you doing beyond state preparation. The best implementation is not just decided at the functional block level, but actually at the system level.
Even simple quantum adders could have multiple implementations. You could build a simple ripple adder, or you can perform quantum arithmetic with the Quantum Fourier Transform (QFT).
This is why the best frameworks for generating quantum circuits will provide multiple options - and perhaps make an optimal choice on their own - instead of forcing you to use a single hard-coded implementation every time.
Because different sorting algorithms are useful in different circumstances.
Some (Bubble sort) are simple to implement, and thus useful for teaching.
Some (Merge sort, Heap sort) have a relatively low limit on the worst-case number of operations they have to perform. If they need to sort N elements, they will never need more than O(N log N). Note: see here for a definition of O()
Some (Insertion sort, Cube sort) have a low number of "best case" operations, meaning that they finish quickly if the list of already sorted. For N elements, their best case will be O(N)
Some (Heap sort) need very little memory
Some (Bead sort) only work with positive integers
Some... well, you get the point.
An engineer that wants to use a sorting algorithm would do well to think about the characteristics of the data that needs to be sorted, the constraints of processor, the availability of parallel processors, and many other constraints. While some algorithms are more popular than others, there is no single algorithm that is best in all circumstances.
The same is true for quantum algorithms.
There is no single implementation of state preparation that's always best. Some use fewer qubits. Some are more accurate. Some have smaller depth. So the choice of the best state preparation depends on the system you are using but also on what else is going on: what else are you doing beyond state preparation. The best implementation is not just decided at the functional block level, but actually at the system level.
Even simple quantum adders could have multiple implementations. You could build a simple ripple adder, or you can perform quantum arithmetic with the Quantum Fourier Transform (QFT).
This is why the best frameworks for generating quantum circuits will provide multiple options - and perhaps make an optimal choice on their own - instead of forcing you to use a single hard-coded implementation every time.