## Step 1: Define the symbolic representation of the variables
The variables are defined as follows:
- $x_0$ : milligrams of magnesium
- $x_1$ : milligrams of vitamin B5
- $x_2$ : grams of carbohydrates
- $x_3$ : milligrams of vitamin B2

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is: $7.73x_0 + 6.54x_1 + 9.04x_2 + 9.4x_3$

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $6x_0 \leq 126$ (cognitive performance index for $x_0$)
- $20x_0 \leq 273$ (energy stability index for $x_0$)
- $14x_1 \leq 126$ (cognitive performance index for $x_1$)
- $14x_1 \leq 273$ (energy stability index for $x_1$)
- $4x_2 \leq 126$ (cognitive performance index for $x_2$)
- $22x_2 \leq 273$ (energy stability index for $x_2$)
- $10x_3 \leq 126$ (cognitive performance index for $x_3$)
- $13x_3 \leq 273$ (energy stability index for $x_3$)
- $4x_2 + 10x_3 \geq 26$ (combined cognitive performance index for $x_2$ and $x_3$)
- $14x_1 + 4x_2 \geq 15$ (combined cognitive performance index for $x_1$ and $x_2$)
- $14x_1 + 10x_3 \geq 25$ (combined cognitive performance index for $x_1$ and $x_3$)
- $6x_0 + 4x_2 \geq 27$ (combined cognitive performance index for $x_0$ and $x_2$)
- $6x_0 + 14x_1 + 4x_2 + 10x_3 \geq 27$ (combined cognitive performance index for all variables)
- $20x_0 + 14x_1 \geq 33$ (combined energy stability index for $x_0$ and $x_1$)
- $20x_0 + 22x_2 \geq 59$ (combined energy stability index for $x_0$ and $x_2$)
- $14x_1 + 22x_2 \geq 47$ (combined energy stability index for $x_1$ and $x_2$)
- $14x_1 + 13x_3 \geq 31$ (combined energy stability index for $x_1$ and $x_3$)
- $20x_0 + 22x_2 + 13x_3 \geq 53$ (combined energy stability index for $x_0$, $x_2$, and $x_3$)
- $20x_0 + 14x_1 + 22x_2 + 13x_3 \geq 53$ (combined energy stability index for all variables)
- $-3x_2 + 2x_3 \geq 0$ (linear constraint)
- $14x_1 + 22x_2 \leq 219$ (upper bound for combined energy stability index of $x_1$ and $x_2$)
- $20x_0 + 22x_2 \leq 116$ (upper bound for combined energy stability index of $x_0$ and $x_2$)
- $14x_1 + 13x_3 \leq 168$ (upper bound for combined energy stability index of $x_1$ and $x_3$)

## 4: Determine variable bounds
- $x_0$ is an integer
- $x_1$ is continuous
- $x_2$ is continuous
- $x_3$ is an integer

## 5: Formulate the problem in Gurobi

```python
import gurobi

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

# Define the variables
x0 = model.addVar(name="milligrams_of_magnesium", vtype=gurobi.GRB.INTEGER)
x1 = model.addVar(name="milligrams_of_vitamin_B5", vtype=gurobi.GRB.CONTINUOUS)
x2 = model.addVar(name="grams_of_carbohydrates", vtype=gurobi.GRB.CONTINUOUS)
x3 = model.addVar(name="milligrams_of_vitamin_B2", vtype=gurobi.GRB.INTEGER)

# Objective function
model.setObjective(7.73 * x0 + 6.54 * x1 + 9.04 * x2 + 9.4 * x3, gurobi.GRB.MINIMIZE)

# Constraints
model.addConstr(6 * x0 <= 126, name="cognitive_performance_index_magnesium")
model.addConstr(20 * x0 <= 273, name="energy_stability_index_magnesium")
model.addConstr(14 * x1 <= 126, name="cognitive_performance_index_vitamin_B5")
model.addConstr(14 * x1 <= 273, name="energy_stability_index_vitamin_B5")
model.addConstr(4 * x2 <= 126, name="cognitive_performance_index_carbohydrates")
model.addConstr(22 * x2 <= 273, name="energy_stability_index_carbohydrates")
model.addConstr(10 * x3 <= 126, name="cognitive_performance_index_vitamin_B2")
model.addConstr(13 * x3 <= 273, name="energy_stability_index_vitamin_B2")

model.addConstr(4 * x2 + 10 * x3 >= 26, name="combined_cognitive_performance_index_carbohydrates_vitamin_B2")
model.addConstr(14 * x1 + 4 * x2 >= 15, name="combined_cognitive_performance_index_vitamin_B5_carbohydrates")
model.addConstr(14 * x1 + 10 * x3 >= 25, name="combined_cognitive_performance_index_vitamin_B5_vitamin_B2")
model.addConstr(6 * x0 + 4 * x2 >= 27, name="combined_cognitive_performance_index_magnesium_carbohydrates")
model.addConstr(6 * x0 + 14 * x1 + 4 * x2 + 10 * x3 >= 27, name="combined_cognitive_performance_index_all")

model.addConstr(20 * x0 + 14 * x1 >= 33, name="combined_energy_stability_index_magnesium_vitamin_B5")
model.addConstr(20 * x0 + 22 * x2 >= 59, name="combined_energy_stability_index_magnesium_carbohydrates")
model.addConstr(14 * x1 + 22 * x2 >= 47, name="combined_energy_stability_index_vitamin_B5_carbohydrates")
model.addConstr(14 * x1 + 13 * x3 >= 31, name="combined_energy_stability_index_vitamin_B5_vitamin_B2")
model.addConstr(20 * x0 + 22 * x2 + 13 * x3 >= 53, name="combined_energy_stability_index_magnesium_carbohydrates_vitamin_B2")
model.addConstr(20 * x0 + 14 * x1 + 22 * x2 + 13 * x3 >= 53, name="combined_energy_stability_index_all")

model.addConstr(-3 * x2 + 2 * x3 >= 0, name="linear_constraint")
model.addConstr(14 * x1 + 22 * x2 <= 219, name="upper_bound_combined_energy_stability_index_vitamin_B5_carbohydrates")
model.addConstr(20 * x0 + 22 * x2 <= 116, name="upper_bound_combined_energy_stability_index_magnesium_carbohydrates")
model.addConstr(14 * x1 + 13 * x3 <= 168, name="upper_bound_combined_energy_stability_index_vitamin_B5_vitamin_B2")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objval)
    print("Milligrams of magnesium: ", x0.varValue)
    print("Milligrams of vitamin B5: ", x1.varValue)
    print("Grams of carbohydrates: ", x2.varValue)
    print("Milligrams of vitamin B2: ", x3.varValue)
else:
    print("The model is infeasible")
```

### Symbolic Representation

```json
{
    'sym_variables': [
        ('x0', 'milligrams of magnesium'), 
        ('x1', 'milligrams of vitamin B5'), 
        ('x2', 'grams of carbohydrates'), 
        ('x3', 'milligrams of vitamin B2')
    ], 
    'objective_function': '7.73*x0 + 6.54*x1 + 9.04*x2 + 9.4*x3', 
    'constraints': [
        '6*x0 <= 126',
        '20*x0 <= 273',
        '14*x1 <= 126',
        '14*x1 <= 273',
        '4*x2 <= 126',
        '22*x2 <= 273',
        '10*x3 <= 126',
        '13*x3 <= 273',
        '4*x2 + 10*x3 >= 26',
        '14*x1 + 4*x2 >= 15',
        '14*x1 + 10*x3 >= 25',
        '6*x0 + 4*x2 >= 27',
        '6*x0 + 14*x1 + 4*x2 + 10*x3 >= 27',
        '20*x0 + 14*x1 >= 33',
        '20*x0 + 22*x2 >= 59',
        '14*x1 + 22*x2 >= 47',
        '14*x1 + 13*x3 >= 31',
        '20*x0 + 22*x2 + 13*x3 >= 53',
        '20*x0 + 14*x1 + 22*x2 + 13*x3 >= 53',
        '-3*x2 + 2*x3 >= 0',
        '14*x1 + 22*x2 <= 219',
        '20*x0 + 22*x2 <= 116',
        '14*x1 + 13*x3 <= 168'
    ]
}
```