To solve this optimization problem, we first need to define the variables, objective function, and constraints using Gurobi's Python interface.

## Step 1: Define the Variables
Let's define the variables for the milligrams of each vitamin: `x0` for vitamin B1, `x1` for vitamin B12, `x2` for vitamin B3, `x3` for vitamin C, and `x4` for vitamin B5.

## Step 2: Define the Objective Function
The objective function to maximize is: $9x_0 + 7x_1 + 5x_2 + 4x_3 + 2x_4$.

## 3: Define the Constraints
We have several constraints based on the given indices and their upper bounds. These include constraints on the individual vitamin contributions to various indices as well as combined constraints.

## 4: Implement in Gurobi
We will implement this in Gurobi using its Python API.

```python
import gurobi as gp

# Define the model
model = gp.Model("vitamin_optimization")

# Define the variables
x0 = model.addVar(lb=0, name="milligrams_of_vitamin_B1")
x1 = model.addVar(lb=0, name="milligrams_of_vitamin_B12")
x2 = model.addVar(lb=0, name="milligrams_of_vitamin_B3")
x3 = model.addVar(lb=0, name="milligrams_of_vitamin_C")
x4 = model.addVar(lb=0, name="milligrams_of_vitamin_B5")

# Define the objective function
model.setObjective(9 * x0 + 7 * x1 + 5 * x2 + 4 * x3 + 2 * x4, gp.GRB.MAXIMIZE)

# Constraints
# Individual vitamin constraints
model.addConstraint(23 * x0 <= 533, name="r0_x0")
model.addConstraint(13 * x0 <= 238, name="r1_x0")
model.addConstraint(27 * x0 <= 493, name="r2_x0")
model.addConstraint(17 * x0 <= 402, name="r3_x0")
model.addConstraint(12 * x0 <= 423, name="r4_x0")

model.addConstraint(26 * x1 <= 533, name="r0_x1")
model.addConstraint(26 * x1 <= 238, name="r1_x1")
model.addConstraint(27 * x1 <= 493, name="r2_x1")
model.addConstraint(11 * x1 <= 402, name="r3_x1")
model.addConstraint(4 * x1 <= 423, name="r4_x1")

model.addConstraint(22 * x2 <= 533, name="r0_x2")
model.addConstraint(4 * x2 <= 238, name="r1_x2")
model.addConstraint(10 * x2 <= 493, name="r2_x2")
model.addConstraint(17 * x2 <= 402, name="r3_x2")
model.addConstraint(10 * x2 <= 423, name="r4_x2")

model.addConstraint(16 * x3 <= 533, name="r0_x3")
model.addConstraint(17 * x3 <= 238, name="r1_x3")
model.addConstraint(27 * x3 <= 493, name="r2_x3")
model.addConstraint(14 * x3 <= 402, name="r3_x3")
model.addConstraint(25 * x3 <= 423, name="r4_x3")

model.addConstraint(4 * x4 <= 533, name="r0_x4")
model.addConstraint(20 * x4 <= 238, name="r1_x4")
model.addConstraint(26 * x4 <= 493, name="r2_x4")
model.addConstraint(2 * x4 <= 402, name="r3_x4")
model.addConstraint(23 * x4 <= 423, name="r4_x4")

# Combined constraints
model.addConstraint(26 * x1 + 16 * x3 >= 58, name="combined_r0_x1_x3")
model.addConstraint(23 * x0 + 26 * x1 >= 55, name="combined_r0_x0_x1")
model.addConstraint(26 * x1 + 22 * x2 >= 91, name="combined_r0_x1_x2")
model.addConstraint(13 * x0 + 26 * x1 >= 24, name="combined_r1_x0_x1")
model.addConstraint(4 * x2 + 17 * x3 >= 43, name="combined_r1_x2_x3")
model.addConstraint(4 * x2 + 20 * x4 >= 24, name="combined_r1_x2_x4")
model.addConstraint(13 * x0 + 17 * x3 >= 32, name="combined_r1_x0_x3")
model.addConstraint(27 * x0 + 10 * x2 + 27 * x3 >= 49, name="combined_r2_x0_x2_x3")
model.addConstraint(17 * x0 + 17 * x2 + 2 * x4 >= 48, name="combined_r3_x0_x2_x4")
model.addConstraint(12 * x0 + 10 * x2 + 23 * x4 >= 42, name="combined_r4_x0_x2_x4")
model.addConstraint(12 * x0 + 10 * x2 + 25 * x3 >= 42, name="combined_r4_x0_x2_x3")
model.addConstraint(4 * x1 + 10 * x2 + 23 * x4 >= 42, name="combined_r4_x1_x2_x4")
model.addConstraint(12 * x0 + 10 * x2 + 23 * x4 >= 64, name="combined_r4_x0_x2_x4_geq_64")
model.addConstraint(12 * x0 + 10 * x2 + 25 * x3 >= 64, name="combined_r4_x0_x2_x3_geq_64")
model.addConstraint(4 * x1 + 10 * x2 + 23 * x4 >= 64, name="combined_r4_x1_x2_x4_geq_64")
model.addConstraint(12 * x0 + 10 * x2 + 23 * x4 >= 82, name="combined_r4_x0_x2_x4_geq_82")
model.addConstraint(12 * x0 + 10 * x2 + 25 * x3 >= 82, name="combined_r4_x0_x2_x3_geq_82")
model.addConstraint(4 * x1 + 10 * x2 + 23 * x4 >= 82, name="combined_r4_x1_x2_x4_geq_82")

model.addConstraint(23 * x0 + 26 * x1 <= 393, name="combined_r0_x0_x1_leq_393")
model.addConstraint(22 * x2 + 16 * x3 <= 455, name="combined_r0_x2_x3_leq_455")
model.addConstraint(23 * x0 + 22 * x2 <= 124, name="combined_r0_x0_x2_leq_124")
model.addConstraint(22 * x2 + 4 * x4 <= 296, name="combined_r0_x2_x4_leq_296")
model.addConstraint(23 * x0 + 16 * x3 + 4 * x4 <= 180, name="combined_r0_x0_x3_x4_leq_180")
model.addConstraint(26 * x1 + 22 * x2 + 16 * x3 <= 483, name="combined_r0_x1_x2_x3_leq_483")
model.addConstraint(23 * x0 + 26 * x1 + 22 * x2 + 16 * x3 + 4 * x4 <= 483, name="combined_r0_all_leq_483")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Milligrams of vitamin B1: {x0.varValue}")
    print(f"Milligrams of vitamin B12: {x1.varValue}")
    print(f"Milligrams of vitamin B3: {x2.varValue}")
    print(f"Milligrams of vitamin C: {x3.varValue}")
    print(f"Milligrams of vitamin B5: {x4.varValue}")
    print(f"Objective: {model.objVal}")
else:
    print("No optimal solution found.")
```