## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_1$ represents 'milligrams of vitamin D'
- $x_2$ represents 'milligrams of vitamin E'
- $x_3$ represents 'milligrams of vitamin B2'
- $x_4$ represents 'milligrams of calcium'

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $5.48x_1 + 3.41x_2 + 3.37x_3 + 9.6x_4$.

## Step 3: Define the immune support index for each variable
The immune support indices are given as:
- $x_1$ has an immune support index of $1.34$
- $x_2$ has an immune support index of $0.07$
- $x_3$ has an immune support index of $1.3$
- $x_4$ has an immune support index of $0.95$

## 4: Translate the constraints into symbolic notation
The constraints are:
1. $0.07x_2 + 0.95x_4 \geq 19$
2. $0.07x_2 + 1.3x_3 \geq 21$
3. $1.34x_1 + 1.3x_3 \geq 23$
4. $0.07x_2 + 1.3x_3 + 0.95x_4 \geq 19$
5. $1.34x_1 + 0.07x_2 + 0.95x_4 \geq 19$
6. $1.34x_1 + 0.07x_2 + 0.95x_4 \geq 19$ (Redundant with constraint 5)
7. $1.34x_1 + 0.07x_2 + 0.95x_4 \geq 19$ (Redundant with constraint 5)
8. $1.34x_1 + 0.07x_2 + 1.3x_3 + 0.95x_4 \geq 19$
9. $-3x_2 + 9x_4 \geq 0$
10. $-x_2 + 9x_3 \geq 0$
11. $9x_1 - 5x_2 \geq 0$
12. $1.34x_1 + 0.95x_4 \leq 45$
13. $1.34x_1 + 1.3x_3 + 0.95x_4 \leq 107$
14. $1.34x_1 + 0.07x_2 + 0.95x_4 \leq 121$

## 5: Remove redundant constraints
After reviewing, constraints 6 and 7 are redundant with constraint 5. Constraint 8 is also redundant with constraint 5.

## 6: Create the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin D'), 
        ('x2', 'milligrams of vitamin E'), 
        ('x3', 'milligrams of vitamin B2'), 
        ('x4', 'milligrams of calcium')
    ], 
    'objective_function': '5.48*x1 + 3.41*x2 + 3.37*x3 + 9.6*x4', 
    'constraints': [
        '0.07*x2 + 0.95*x4 >= 19',
        '0.07*x2 + 1.3*x3 >= 21',
        '1.34*x1 + 1.3*x3 >= 23',
        '0.07*x2 + 1.3*x3 + 0.95*x4 >= 19',
        '1.34*x1 + 0.07*x2 + 0.95*x4 >= 19',
        '1.34*x1 + 0.07*x2 + 1.3*x3 + 0.95*x4 >= 19',
        '-3*x2 + 9*x4 >= 0',
        '-x2 + 9*x3 >= 0',
        '9*x1 - 5*x2 >= 0',
        '1.34*x1 + 0.95*x4 <= 45',
        '1.34*x1 + 1.3*x3 + 0.95*x4 <= 107',
        '1.34*x1 + 0.07*x2 + 0.95*x4 <= 121'
    ]
}
```

## 7: Write the Gurobi code
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x1")  # milligrams of vitamin D
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x2")  # milligrams of vitamin E
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x3")  # milligrams of vitamin B2
    x4 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x4")  # milligrams of calcium

    # Objective function
    model.setObjective(5.48*x1 + 3.41*x2 + 3.37*x3 + 9.6*x4, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(0.07*x2 + 0.95*x4 >= 19)
    model.addConstr(0.07*x2 + 1.3*x3 >= 21)
    model.addConstr(1.34*x1 + 1.3*x3 >= 23)
    model.addConstr(0.07*x2 + 1.3*x3 + 0.95*x4 >= 19)
    model.addConstr(1.34*x1 + 0.07*x2 + 0.95*x4 >= 19)
    model.addConstr(1.34*x1 + 0.07*x2 + 1.3*x3 + 0.95*x4 >= 19)
    model.addConstr(-3*x2 + 9*x4 >= 0)
    model.addConstr(-x2 + 9*x3 >= 0)
    model.addConstr(9*x1 - 5*x2 >= 0)
    model.addConstr(1.34*x1 + 0.95*x4 <= 45)
    model.addConstr(1.34*x1 + 1.3*x3 + 0.95*x4 <= 107)
    model.addConstr(1.34*x1 + 0.07*x2 + 0.95*x4 <= 121)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"milligrams of vitamin D: {x1.varValue}")
        print(f"milligrams of vitamin E: {x2.varValue}")
        print(f"milligrams of vitamin B2: {x3.varValue}")
        print(f"milligrams of calcium: {x4.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```