## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of iron' and 'grams of protein', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Define the symbolic representation of the objective function
The objective function to minimize is $3x_1 + 2x_2$.

## Step 3: Define the symbolic representation of the constraints
The constraints are:
- $4x_1 + 6x_2 \geq 14$
- $11x_1 + 7x_2 \geq 48$
- $15x_1 + 15x_2 \geq 26$
- $2x_1 + 14x_2 \geq 22$
- $8x_1 + 12x_2 \geq 30$
- $x_1 - 5x_2 \geq 0$
- $4x_1 + 6x_2 \leq 32$
- $11x_1 + 7x_2 \leq 95$
- $15x_1 + 15x_2 \leq 37$
- $2x_1 + 14x_2 \leq 35$
- $8x_1 + 12x_2 \leq 67$

## 4: Convert the problem into a Gurobi code
We will use the Gurobi Python API to model and solve this optimization problem.

```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="milligrams_of_iron", lb=0)  # Assuming non-negative
x2 = model.addVar(name="grams_of_protein", lb=0)  # Assuming non-negative

# Define the objective function
model.setObjective(3 * x1 + 2 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(4 * x1 + 6 * x2 >= 14, name="muscle_growth_index")
model.addConstr(11 * x1 + 7 * x2 >= 48, name="cognitive_performance_index")
model.addConstr(15 * x1 + 15 * x2 >= 26, name="cardiovascular_support_index")
model.addConstr(2 * x1 + 14 * x2 >= 22, name="kidney_support_index")
model.addConstr(8 * x1 + 12 * x2 >= 30, name="digestive_support_index")
model.addConstr(x1 - 5 * x2 >= 0, name="iron_protein_balance")
model.addConstr(4 * x1 + 6 * x2 <= 32, name="muscle_growth_index_max")
model.addConstr(11 * x1 + 7 * x2 <= 95, name="cognitive_performance_index_max")
model.addConstr(15 * x1 + 15 * x2 <= 37, name="cardiovascular_support_index_max")
model.addConstr(2 * x1 + 14 * x2 <= 35, name="kidney_support_index_max")
model.addConstr(8 * x1 + 12 * x2 <= 67, name="digestive_support_index_max")

# Optimize the model
model.optimize()

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

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'milligrams of iron'), ('x2', 'grams of protein')],
    'objective_function': '3*x1 + 2*x2',
    'constraints': [
        '4*x1 + 6*x2 >= 14',
        '11*x1 + 7*x2 >= 48',
        '15*x1 + 15*x2 >= 26',
        '2*x1 + 14*x2 >= 22',
        '8*x1 + 12*x2 >= 30',
        'x1 - 5*x2 >= 0',
        '4*x1 + 6*x2 <= 32',
        '11*x1 + 7*x2 <= 95',
        '15*x1 + 15*x2 <= 37',
        '2*x1 + 14*x2 <= 35',
        '8*x1 + 12*x2 <= 67'
    ]
}
```