## Step 1: Define the symbolic representation of the problem
The problem involves three variables: 'milligrams of zinc', 'milligrams of magnesium', and 'milligrams of vitamin B4', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $5.01x_0 + 2.79x_1 + 4.52x_2$.

## Step 3: List the constraints in symbolic notation
The constraints given are:
- Digestive support index constraints:
  - $8x_0 + 7x_1 + 26x_2 \geq 39$ (total combined digestive support index from $x_1$ and $x_2$)
  - $16x_0 + 8x_1 \geq 21$ (total combined digestive support index from $x_0$ and $x_1$)
  - $16x_0 + 8x_1 + 21x_2 \geq 45$ (total combined digestive support index from $x_0$, $x_1$, and $x_2$)
  - $16x_0 + 8x_1 + 21x_2 \geq 45$ is redundant with the previous one.

- Immune support index constraints:
  - $7x_1 + 26x_2 \geq 72$ (total combined immune support index from $x_1$ and $x_2$)
  - $8x_0 + 7x_1 \geq 33$ (total combined immune support index from $x_0$ and $x_1$)
  - $8x_0 + 7x_1 + 26x_2 \geq 33$ (total combined immune support index from $x_0$, $x_1$, and $x_2$)
  - $8x_0 + 26x_2 \leq 233$ (total combined immune support index from $x_0$ and $x_2$)
  - $8x_0 + 7x_1 \leq 231$ (total combined immune support index from $x_0$ and $x_1$)

- Muscle growth index constraints:
  - $12x_0 + 21x_2 \geq 32$ (total combined muscle growth index from $x_0$ and $x_2$)
  - $12x_0 + 19x_1 \geq 48$ (total combined muscle growth index from $x_0$ and $x_1$)
  - $12x_0 + 19x_1 + 21x_2 \leq 81$ (total combined muscle growth index from $x_0$, $x_1$, and $x_2$)

- Other constraints:
  - $4x_0 - 2x_2 \geq 0$
  - $-7x_0 + 7x_1 \geq 0$

## 4: Create a symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of zinc'), 
        ('x1', 'milligrams of magnesium'), 
        ('x2', 'milligrams of vitamin B4')
    ], 
    'objective_function': '5.01*x0 + 2.79*x1 + 4.52*x2', 
    'constraints': [
        '8*x0 + 8*x1 + 21*x2 >= 39', 
        '16*x0 + 8*x1 >= 21', 
        '16*x0 + 8*x1 + 21*x2 >= 45', 
        '7*x1 + 26*x2 >= 72', 
        '8*x0 + 7*x1 >= 33', 
        '8*x0 + 7*x1 + 26*x2 >= 33', 
        '8*x0 + 26*x2 <= 233', 
        '8*x0 + 7*x1 <= 231', 
        '12*x0 + 21*x2 >= 32', 
        '12*x0 + 19*x1 >= 48', 
        '12*x0 + 19*x1 + 21*x2 <= 81', 
        '4*x0 - 2*x2 >= 0', 
        '-7*x0 + 7*x1 >= 0'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

def optimize_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="milligrams_of_zinc", lb=0)
    x1 = model.addVar(name="milligrams_of_magnesium", lb=0)
    x2 = model.addVar(name="milligrams_of_vitamin_B4", lb=0)

    # Objective function
    model.setObjective(5.01*x0 + 2.79*x1 + 4.52*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(8*x0 + 8*x1 + 21*x2 >= 39)
    model.addConstr(16*x0 + 8*x1 >= 21)
    model.addConstr(16*x0 + 8*x1 + 21*x2 >= 45)
    model.addConstr(7*x1 + 26*x2 >= 72)
    model.addConstr(8*x0 + 7*x1 >= 33)
    model.addConstr(8*x0 + 7*x1 + 26*x2 >= 33)
    model.addConstr(8*x0 + 26*x2 <= 233)
    model.addConstr(8*x0 + 7*x1 <= 231)
    model.addConstr(12*x0 + 21*x2 >= 32)
    model.addConstr(12*x0 + 19*x1 >= 48)
    model.addConstr(12*x0 + 19*x1 + 21*x2 <= 81)
    model.addConstr(4*x0 - 2*x2 >= 0)
    model.addConstr(-7*x0 + 7*x1 >= 0)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of zinc: {x0.varValue}")
        print(f"Milligrams of magnesium: {x1.varValue}")
        print(f"Milligrams of vitamin B4: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```