To solve the optimization problem described, we will use the Gurobi Python API. The goal is to maximize the objective function that combines milligrams of calcium, grams of protein, and milligrams of vitamin B9, subject to several constraints related to their kidney support indices.

Here's how we can break down the problem:

1. **Objective Function**: Maximize \(9 \times \text{milligrams of calcium} + 2 \times \text{grams of protein} + 8 \times \text{milligrams of vitamin B9}\).

2. **Constraints**:
   - The kidney support index for milligrams of calcium is 7.69.
   - Grams of protein have a kidney support index of 6.48.
   - Milligrams of vitamin B9 each have a kidney support index of 3.53.
   - Total combined kidney support index from grams of protein plus milligrams of vitamin B9 should be no less than 47.
   - Total combined kidney support index from milligrams of calcium, grams of protein, and milligrams of vitamin B9 has to be at minimum 64.
   - Total combined kidney support index from grams of protein and milligrams of vitamin B9 must be 157 or less.
   - Total combined kidney support index from milligrams of calcium and milligrams of vitamin B9 has to be 158 at a maximum.
   - Total combined kidney support index from milligrams of calcium plus grams of protein should be equal to or less than 192.
   - Total combined kidney support index from milligrams of calcium, grams of protein, and milligrams of vitamin B9 should be at most 192.

Given the problem description, we can directly translate these requirements into Gurobi variables and constraints. Note that all variables are continuous since the problem allows for non-integer amounts of each substance.

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
calcium = m.addVar(lb=0, name="milligrams_of_calcium")
protein = m.addVar(lb=0, name="grams_of_protein")
vitamin_b9 = m.addVar(lb=0, name="milligrams_of_vitamin_B9")

# Objective function: Maximize
m.setObjective(9*calcium + 2*protein + 8*vitamin_b9, GRB.MAXIMIZE)

# Constraints
m.addConstr(protein * 6.48 + vitamin_b9 * 3.53 >= 47, name="constraint_1")
m.addConstr(calcium * 7.69 + protein * 6.48 + vitamin_b9 * 3.53 >= 64, name="constraint_2")
m.addConstr(protein * 6.48 + vitamin_b9 * 3.53 <= 157, name="constraint_3")
m.addConstr(calcium * 7.69 + vitamin_b9 * 3.53 <= 158, name="constraint_4")
m.addConstr(calcium * 7.69 + protein * 6.48 <= 192, name="constraint_5")
m.addConstr(calcium * 7.69 + protein * 6.48 + vitamin_b9 * 3.53 <= 192, name="constraint_6")

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Calcium: {calcium.x}")
    print(f"Grams of Protein: {protein.x}")
    print(f"Milligrams of Vitamin B9: {vitamin_b9.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```