## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_0$ represents 'milligrams of magnesium'
- $x_1$ represents 'milligrams of vitamin B4'
- $x_2$ represents 'milligrams of vitamin D'
- $x_3$ represents 'grams of fat'

## Step 2: Define the symbolic representation of the objective function
The objective function to minimize is $8.32x_0 + 2.42x_1 + 1.56x_2 + 8.06x_3$.

## Step 3: Define the symbolic representation of the constraints
The constraints are:
- $7x_0 \geq 7$ (cognitive performance index of milligrams of magnesium, but this seems to be an equality based on the problem statement: $7x_0 = 7$)
- $8x_0 \geq 8$ (energy stability index of milligrams of magnesium, but this seems to be an equality based on the problem statement: $8x_0 = 8$)
- $11x_1 \geq 11$ (cognitive performance index of milligrams of vitamin B4, but this seems to be an equality based on the problem statement: $11x_1 = 11$)
- $8x_1 \geq 8$ (energy stability index of milligrams of vitamin B4, but this seems to be an equality based on the problem statement: $8x_1 = 8$)
- $5x_2 \geq 5$ (cognitive performance index of milligrams of vitamin D, but this seems to be an equality based on the problem statement: $5x_2 = 5$)
- $2x_2 \geq 2$ (energy stability index of milligrams of vitamin D, but this seems to be an equality based on the problem statement: $2x_2 = 2$)
- $9x_3 \geq 9$ (cognitive performance index for grams of fat, but this seems to be an equality based on the problem statement: $9x_3 = 9$)
- $6x_3 \geq 6$ (energy stability index for grams of fat, but this seems to be an equality based on the problem statement: $6x_3 = 6$)
- $5x_2 + 9x_3 \geq 26$ 
- $11x_1 + 5x_2 \geq 30$
- $7x_0 + 11x_1 \geq 34$
- $11x_1 + 9x_3 \geq 18$
- $7x_0 + 5x_2 \geq 16$
- $7x_0 + 9x_3 \geq 21$
- $7x_0 + 11x_1 + 5x_2 + 9x_3 \geq 21$
- $8x_0 + 2x_2 \geq 19$
- $8x_1 + 6x_3 \geq 21$
- $8x_0 + 2x_2 + 6x_3 \geq 17$
- $8x_0 + 8x_1 + 2x_2 + 6x_3 \geq 17$
- $7x_0 - 5x_1 \geq 0$
- $-8x_0 + 5x_2 \geq 0$

## 4: Correct and Clarify Constraints
Upon closer inspection, it seems some constraints were initially misinterpreted as inequalities when they might have been meant as equalities based on the problem description. However, sticking strictly to the problem description:
- The cognitive performance index constraints and others should be directly used as given.

## 5: Formulate the Problem in Gurobi
We will use Gurobi to solve this linear programming problem.

```python
import gurobi as gp

# Define the model
m = gp.Model()

# Define the variables
x0 = m.addVar(name="milligrams_of_magnesium", vtype=gp.GRB.INTEGER)
x1 = m.addVar(name="milligrams_of_vitamin_B4", vtype=gp.GRB.INTEGER)
x2 = m.addVar(name="milligrams_of_vitamin_D", vtype=gp.GRB.INTEGER)
x3 = m.addVar(name="grams_of_fat")

# Objective function
m.setObjective(8.32*x0 + 2.42*x1 + 1.56*x2 + 8.06*x3, gp.GRB.MINIMIZE)

# Constraints
m.addConstr(x0 == 7, name="cognitive_performance_magnesium")
m.addConstr(8*x0 == 8, name="energy_stability_magnesium")
m.addConstr(11*x1 == 11, name="cognitive_performance_vitamin_B4")
m.addConstr(8*x1 == 8, name="energy_stability_vitamin_B4")
m.addConstr(5*x2 == 5, name="cognitive_performance_vitamin_D")
m.addConstr(2*x2 == 2, name="energy_stability_vitamin_D")
m.addConstr(9*x3 == 9, name="cognitive_performance_fat")
m.addConstr(6*x3 == 6, name="energy_stability_fat")

m.addConstr(5*x2 + 9*x3 >= 26, name="combined_cognitive_vitamin_D_fat")
m.addConstr(11*x1 + 5*x2 >= 30, name="combined_cognitive_vitamin_B4_vitamin_D")
m.addConstr(7*x0 + 11*x1 >= 34, name="combined_cognitive_magnesium_vitamin_B4")
m.addConstr(11*x1 + 9*x3 >= 18, name="combined_cognitive_vitamin_B4_fat")
m.addConstr(7*x0 + 5*x2 >= 16, name="combined_cognitive_magnesium_vitamin_D")
m.addConstr(7*x0 + 9*x3 >= 21, name="combined_cognitive_magnesium_fat")
m.addConstr(7*x0 + 11*x1 + 5*x2 + 9*x3 >= 21, name="combined_cognitive_all")

m.addConstr(8*x0 + 2*x2 >= 19, name="combined_energy_magnesium_vitamin_D")
m.addConstr(8*x1 + 6*x3 >= 21, name="combined_energy_vitamin_B4_fat")
m.addConstr(8*x0 + 2*x2 + 6*x3 >= 17, name="combined_energy_magnesium_vitamin_D_fat")
m.addConstr(8*x0 + 8*x1 + 2*x2 + 6*x3 >= 17, name="combined_energy_all")

m.addConstr(7*x0 - 5*x1 >= 0, name="magnesium_vitamin_B4_constraint")
m.addConstr(-8*x0 + 5*x2 >= 0, name="magnesium_vitamin_D_constraint")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objval)
    print("milligrams of magnesium: ", x0.varValue)
    print("milligrams of vitamin B4: ", x1.varValue)
    print("milligrams of vitamin D: ", x2.varValue)
    print("grams of fat: ", x3.varValue)
else:
    print("The model is infeasible")
```

## 6: Symbolic Representation
The symbolic representation is as follows:

```json
{
    'sym_variables': [
        ('x0', 'milligrams of magnesium'), 
        ('x1', 'milligrams of vitamin B4'), 
        ('x2', 'milligrams of vitamin D'), 
        ('x3', 'grams of fat')
    ], 
    'objective_function': '8.32*x0 + 2.42*x1 + 1.56*x2 + 8.06*x3', 
    'constraints': [
        'x0 = 7',
        '8*x0 = 8',
        '11*x1 = 11',
        '8*x1 = 8',
        '5*x2 = 5',
        '2*x2 = 2',
        '9*x3 = 9',
        '6*x3 = 6',
        '5*x2 + 9*x3 >= 26',
        '11*x1 + 5*x2 >= 30',
        '7*x0 + 11*x1 >= 34',
        '11*x1 + 9*x3 >= 18',
        '7*x0 + 5*x2 >= 16',
        '7*x0 + 9*x3 >= 21',
        '7*x0 + 11*x1 + 5*x2 + 9*x3 >= 21',
        '8*x0 + 2*x2 >= 19',
        '8*x1 + 6*x3 >= 21',
        '8*x0 + 2*x2 + 6*x3 >= 17',
        '8*x0 + 8*x1 + 2*x2 + 6*x3 >= 17',
        '7*x0 - 5*x1 >= 0',
        '-8*x0 + 5*x2 >= 0'
    ]
}
```