To solve this optimization problem using Gurobi, we need to define the variables and the objective function, as well as all the constraints provided in the problem description.

Let's denote:
- $x_0$ as the quantity of milligrams of vitamin B9,
- $x_1$ as the quantity of milligrams of magnesium,
- $x_2$ as the quantity of milligrams of calcium.

The objective function to minimize is: $8x_0 + 7x_1 + 7x_2$.

From the given constraints:
- Digestive support indices for each component are given.
- Kidney support indices for each component are given.
- Various combinations of these components have minimum and maximum requirements for both digestive and kidney support indices.
- Some linear inequalities involving $x_0$, $x_1$, and $x_2$ must be satisfied.

Given the detailed problem description, let's translate this into Gurobi code in Python. Note that we will use the `gurobipy` library to model and solve this optimization problem.


```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(name='milligrams_of_vitamin_B9', lb=0)  # Non-negative, can be fractional
x1 = m.addVar(name='milligrams_of_magnesium', lb=0)   # Non-negative, can be fractional
x2 = m.addVar(name='milligrams_of_calcium', lb=0)     # Non-negative, can be fractional

# Objective function: Minimize 8*x0 + 7*x1 + 7*x2
m.setObjective(8*x0 + 7*x1 + 7*x2, GRB.MINIMIZE)

# Constraints:
# Digestive support index constraints
m.addConstr(9*x0 + 13*x1 + 12*x2 >= 56, name='total_digestive_support_min')
m.addConstr(9*x0 + 13*x1 + 12*x2 <= 255, name='total_digestive_support_max')
m.addConstr(9*x0 + 12*x2 >= 35, name='vitamin_b9_and_calcium_digestive_support_min')
m.addConstr(13*x1 + 12*x2 <= 213, name='magnesium_and_calcium_digestive_support_max')
m.addConstr(9*x0 + 12*x2 <= 104, name='vitamin_b9_and_calcium_digestive_support_max')

# Kidney support index constraints
m.addConstr(5*x0 + 17*x2 >= 112, name='vitamin_b9_and_calcium_kidney_support_min')
m.addConstr(23*x1 + 17*x2 >= 79, name='magnesium_and_calcium_kidney_support_min')
m.addConstr(5*x0 + 23*x1 >= 112, name='vitamin_b9_and_magnesium_kidney_support_min')
m.addConstr(5*x0 + 23*x1 + 17*x2 >= 112, name='total_kidney_support_min')

# Additional linear inequalities
m.addConstr(-3*x1 + 8*x2 >= 0, name='magnesium_and_calcium_inequality')
m.addConstr(-3*x0 + 10*x2 >= 0, name='vitamin_b9_and_calcium_inequality')

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of Vitamin B9: {x0.x}")
    print(f"Milligrams of Magnesium: {x1.x}")
    print(f"Milligrams of Calcium: {x2.x}")
else:
    print("No optimal solution found.")
```