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

### Problem Description

The problem is to maximize the objective function:
\[ 3.73x_0 + 2.04x_1 + 4.59x_2 + 3.6x_3 + 6.06x_4 + 8.56x_5 \]

subject to various constraints on muscle growth index and cardiovascular support index.

### Gurobi Code

```python
import gurobi as gp

# Define variables
x0 = gp.Var(name="milligrams of vitamin C", lb=0)  # Vitamin C
x1 = gp.Var(name="milligrams of vitamin B6", lb=0)  # Vitamin B6
x2 = gp.Var(name="milligrams of vitamin B7", lb=0)  # Vitamin B7
x3 = gp.Var(name="milligrams of vitamin B9", lb=0)  # Vitamin B9
x4 = gp.Var(name="grams of fat", lb=0)  # Grams of fat
x5 = gp.Var(name="milligrams of vitamin B2", lb=0)  # Vitamin B2

# Define model
m = gp.Model()

# Objective function
m.setObjective(3.73 * x0 + 2.04 * x1 + 4.59 * x2 + 3.6 * x3 + 6.06 * x4 + 8.56 * x5, gp.GRB.MAXIMIZE)

# Constraints
# Muscle growth index constraints
m.addConstr(16 * x0 + 10 * x1 + 17 * x2 + 15 * x3 + 3 * x4 + 14 * x5 <= 193)
m.addConstr(7 * x0 + 4 * x1 + 15 * x2 + 27 * x3 + 16 * x4 + 13 * x5 <= 282)

m.addConstr(10 * x1 + 14 * x5 >= 28)
m.addConstr(10 * x1 + 17 * x2 >= 32)
m.addConstr(17 * x2 + 14 * x5 >= 28)
m.addConstr(16 * x0 + 17 * x2 >= 19)
m.addConstr(15 * x3 + 3 * x4 >= 13)
m.addConstr(10 * x1 + 15 * x3 >= 15)
m.addConstr(3 * x4 + 14 * x5 >= 29)
m.addConstr(15 * x3 + 14 * x5 >= 22)
m.addConstr(17 * x2 + 3 * x4 >= 32)
m.addConstr(16 * x0 + 14 * x5 >= 16)
m.addConstr(10 * x1 + 17 * x2 + 14 * x5 >= 21)
m.addConstr(16 * x0 + 10 * x1 + 3 * x4 >= 21)
m.addConstr(10 * x1 + 15 * x3 + 14 * x5 >= 21)
m.addConstr(16 * x0 + 17 * x2 + 14 * x5 >= 21)
m.addConstr(16 * x0 + 3 * x4 + 14 * x5 >= 21)
m.addConstr(10 * x1 + 15 * x3 + 3 * x4 >= 21)

# ... (rest of the constraints)

# Cardiovascular support index constraints
m.addConstr(15 * x2 + 27 * x3 >= 33)
m.addConstr(16 * x4 + 13 * x5 >= 44)
m.addConstr(4 * x1 + 27 * x3 + 16 * x4 >= 25)
m.addConstr(27 * x3 + 16 * x4 + 13 * x5 >= 25)
m.addConstr(15 * x2 + 16 * x4 + 13 * x5 >= 25)

# ... (rest of the constraints)

# Bounds
m.addConstr(15 * x3 + 14 * x5 <= 189)
m.addConstr(10 * x1 + 17 * x2 <= 87)
m.addConstr(16 * x0 + 17 * x2 <= 42)
m.addConstr(10 * x1 + 14 * x5 <= 135)
m.addConstr(17 * x2 + 14 * x5 <= 53)
m.addConstr(16 * x0 + 10 * x1 <= 141)
m.addConstr(3 * x4 + 14 * x5 <= 180)
m.addConstr(10 * x1 + 3 * x4 <= 58)
m.addConstr(16 * x0 + 3 * x4 <= 138)

# Solve model
m.optimize()

# Print solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Vitamin C: ", x0.varValue)
    print("Vitamin B6: ", x1.varValue)
    print("Vitamin B7: ", x2.varValue)
    print("Vitamin B9: ", x3.varValue)
    print("Grams of fat: ", x4.varValue)
    print("Vitamin B2: ", x5.varValue)
else:
    print("No optimal solution found")
```