## Step 1: Define the symbolic representation of the problem
The problem involves three variables: 'milligrams of vitamin B2', 'milligrams of iron', and 'milligrams of vitamin B12'. Let's denote these as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $6x_1 + 2x_2 + 6x_3$.

## 3: Define the constraints in symbolic notation
The constraints given are:
- Cognitive performance index constraints:
  - $2x_1 + 11x_2 + 2x_3 \geq 17$
  - $2x_1 + 2x_3 \geq 25$
  - $2x_1 + 11x_2 + 2x_3 \geq 25$
  - $2x_1 + 11x_2 + 2x_3 \geq 25$ (Redundant with the previous one)
- Energy stability index constraints:
  - $10x_1 + 9x_2 + 5x_3 \geq 13$
  - $10x_1 + 9x_2 \geq 13$
  - $10x_1 + 9x_2 + 5x_3 \geq 13$ (Redundant with the previous one)
  - $9x_2 + 5x_3 \geq 5$
- Immune support index constraints:
  - $7x_1 + x_2 + 6x_3 \geq 13$
  - $7x_1 + 6x_3 \geq 13$
  - $7x_1 + x_2 + 6x_3 \geq 13$ (Redundant with the previous one)
  - $x_2 + 6x_3 \geq 20$
- Other constraints:
  - $-9x_1 + 5x_3 \geq 0$
  - $2x_1 + 2x_3 \leq 58$
  - $10x_1 + 9x_2 + 5x_3 \leq 32$

## 4: Remove redundant constraints and simplify
After reviewing, the non-redundant constraints are:
- $2x_1 + 11x_2 + 2x_3 \geq 17$
- $2x_1 + 2x_3 \geq 25$
- $10x_1 + 9x_2 \geq 13$
- $9x_2 + 5x_3 \geq 5$
- $7x_1 + x_2 + 6x_3 \geq 13$
- $x_2 + 6x_3 \geq 20$
- $-9x_1 + 5x_3 \geq 0$
- $2x_1 + 2x_3 \leq 58$
- $10x_1 + 9x_2 + 5x_3 \leq 32$

## 5: Write down the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B2'), ('x2', 'milligrams of iron'), ('x3', 'milligrams of vitamin B12')],
    'objective_function': '6*x1 + 2*x2 + 6*x3',
    'constraints': [
        '2*x1 + 11*x2 + 2*x3 >= 17',
        '2*x1 + 2*x3 >= 25',
        '10*x1 + 9*x2 >= 13',
        '9*x2 + 5*x3 >= 5',
        '7*x1 + x2 + 6*x3 >= 13',
        'x2 + 6*x3 >= 20',
        '-9*x1 + 5*x3 >= 0',
        '2*x1 + 2*x3 <= 58',
        '10*x1 + 9*x2 + 5*x3 <= 32'
    ]
}
```

## 6: Implement the problem in Gurobi
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(name="x1", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B2
    x2 = model.addVar(name="x2", lb=-gurobi.GRB.INFINITY)  # milligrams of iron
    x3 = model.addVar(name="x3", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B12

    # Objective function
    model.setObjective(6 * x1 + 2 * x2 + 6 * x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(2 * x1 + 11 * x2 + 2 * x3 >= 17)
    model.addConstr(2 * x1 + 2 * x3 >= 25)
    model.addConstr(10 * x1 + 9 * x2 >= 13)
    model.addConstr(9 * x2 + 5 * x3 >= 5)
    model.addConstr(7 * x1 + x2 + 6 * x3 >= 13)
    model.addConstr(x2 + 6 * x3 >= 20)
    model.addConstr(-9 * x1 + 5 * x3 >= 0)
    model.addConstr(2 * x1 + 2 * x3 <= 58)
    model.addConstr(10 * x1 + 9 * x2 + 5 * x3 <= 32)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B2: {x1.varValue}")
        print(f"Milligrams of iron: {x2.varValue}")
        print(f"Milligrams of vitamin B12: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```