## Step 1: Define the optimization problem and its constraints
The problem requires minimizing the objective function: $7.73x_0 + 6.54x_1 + 9.04x_2 + 9.4x_3$, where $x_0$ represents milligrams of magnesium, $x_1$ represents milligrams of vitamin B5, $x_2$ represents grams of carbohydrates, and $x_3$ represents milligrams of vitamin B2.

## 2: List all the constraints
The constraints given are:
- $6x_0 \leq 126$
- $20x_0 \leq 273$
- $14x_1 \leq 126$
- $14x_1 \leq 273$
- $4x_2 \leq 126$
- $22x_2 \leq 273$
- $10x_3 \leq 126$
- $13x_3 \leq 273$
- $4x_2 + 10x_3 \geq 26$
- $14x_1 + 4x_2 \geq 15$
- $14x_1 + 10x_3 \geq 25$
- $6x_0 + 4x_2 \geq 27$
- $6x_0 + 14x_1 + 4x_2 + 10x_3 \geq 27$
- $20x_0 + 14x_1 \geq 33$
- $20x_0 + 22x_2 \geq 59$
- $14x_1 + 22x_2 \geq 47$
- $14x_1 + 13x_3 \geq 31$
- $20x_0 + 22x_2 + 13x_3 \geq 53$
- $20x_0 + 14x_1 + 22x_2 + 13x_3 \geq 53$
- $-3x_2 + 2x_3 \geq 0$
- $14x_1 + 22x_2 \leq 219$
- $20x_0 + 22x_2 \leq 116$
- $14x_1 + 13x_3 \leq 168$
- $x_0$ is an integer
- $x_1$ is continuous
- $x_2$ is continuous
- $x_3$ is an integer

## 3: Convert the problem into Gurobi code
```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)  # integer
x1 = m.addVar(name="milligrams_of_vitamin_B5", vtype=gp.GRB.CONTINUOUS)  # continuous
x2 = m.addVar(name="grams_of_carbohydrates", vtype=gp.GRB.CONTINUOUS)  # continuous
x3 = m.addVar(name="milligrams_of_vitamin_B2", vtype=gp.GRB.INTEGER)  # integer

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

# Constraints
m.addConstr(6 * x0 <= 126, name="cognitive_performance_magnesium")
m.addConstr(20 * x0 <= 273, name="energy_stability_magnesium")
m.addConstr(14 * x1 <= 126, name="cognitive_performance_vitamin_B5")
m.addConstr(14 * x1 <= 273, name="energy_stability_vitamin_B5")
m.addConstr(4 * x2 <= 126, name="cognitive_performance_carbohydrates")
m.addConstr(22 * x2 <= 273, name="energy_stability_carbohydrates")
m.addConstr(10 * x3 <= 126, name="cognitive_performance_vitamin_B2")
m.addConstr(13 * x3 <= 273, name="energy_stability_vitamin_B2")

m.addConstr(4 * x2 + 10 * x3 >= 26, name="combined_cognitive_performance_B2_carbohydrates")
m.addConstr(14 * x1 + 4 * x2 >= 15, name="combined_cognitive_performance_vitamin_B5_carbohydrates")
m.addConstr(14 * x1 + 10 * x3 >= 25, name="combined_cognitive_performance_vitamin_B5_B2")
m.addConstr(6 * x0 + 4 * x2 >= 27, name="combined_cognitive_performance_magnesium_carbohydrates")
m.addConstr(6 * x0 + 14 * x1 + 4 * x2 + 10 * x3 >= 27, name="combined_cognitive_performance_all")

m.addConstr(20 * x0 + 14 * x1 >= 33, name="combined_energy_stability_magnesium_vitamin_B5")
m.addConstr(20 * x0 + 22 * x2 >= 59, name="combined_energy_stability_magnesium_carbohydrates")
m.addConstr(14 * x1 + 22 * x2 >= 47, name="combined_energy_stability_vitamin_B5_carbohydrates")
m.addConstr(14 * x1 + 13 * x3 >= 31, name="combined_energy_stability_vitamin_B5_B2")
m.addConstr(20 * x0 + 22 * x2 + 13 * x3 >= 53, name="combined_energy_stability_magnesium_carbohydrates_B2")
m.addConstr(20 * x0 + 14 * x1 + 22 * x2 + 13 * x3 >= 53, name="combined_energy_stability_all")

m.addConstr(-3 * x2 + 2 * x3 >= 0, name="carbohydrates_B2_relation")

m.addConstr(14 * x1 + 22 * x2 <= 219, name="max_energy_stability_vitamin_B5_carbohydrates")
m.addConstr(20 * x0 + 22 * x2 <= 116, name="max_energy_stability_magnesium_carbohydrates")
m.addConstr(14 * x1 + 13 * x3 <= 168, name="max_energy_stability_vitamin_B5_B2")

# 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 B5: ", x1.varValue)
    print("Grams of carbohydrates: ", x2.varValue)
    print("Milligrams of vitamin B2: ", x3.varValue)
else:
    print("The model is infeasible")
```