## Step 1: Define the optimization problem
The goal is to maximize the objective function $8 \times \text{grams of carbohydrates} + 3 \times \text{milligrams of zinc} + 2 \times \text{milligrams of vitamin K}$, subject to various constraints.

## Step 2: List all the constraints
1. $\text{cognitive performance index of grams of carbohydrates} = 12.5$
2. $\text{kidney support index of grams of carbohydrates} = 11.97$
3. $\text{cognitive performance index of milligrams of zinc} = 19.96$
4. $\text{kidney support index of milligrams of zinc} = 20.9$
5. $\text{cognitive performance index of milligrams of vitamin K} = 12.75$
6. $\text{kidney support index of milligrams of vitamin K} = 6.86$
7. Total cognitive performance index $\geq 58$
8. Total kidney support index $\geq 79$
9. $-\text{milligrams of zinc} + \text{milligrams of vitamin K} \geq 0$
10. Total cognitive performance index of grams of carbohydrates and milligrams of zinc $\leq 132$
11. Total cognitive performance index of milligrams of zinc and milligrams of vitamin K $\leq 145$
12. Total cognitive performance index of all $\leq 90$
13. Total cognitive performance index of all $= 90$ (This seems to be a repeated constraint with a specific value)
14. Total kidney support index of milligrams of zinc and milligrams of vitamin K $\leq 236$
15. Total kidney support index of grams of carbohydrates and milligrams of zinc $\leq 251$
16. Total kidney support index of all $\leq 251$

## 3: Formulate the problem in Gurobi
We will use Gurobi to solve this optimization problem. The variables are $x_0$ (grams of carbohydrates), $x_1$ (milligrams of zinc), and $x_2$ (milligrams of vitamin K).

## 4: Write the Gurobi code
```python
import gurobi

def optimize_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define the variables
    x0 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="grams_of_carbohydrates")
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams_of_zinc")
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams_of_vitamin_K")

    # Objective function
    model.setObjective(8 * x0 + 3 * x1 + 2 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    # 7. Total cognitive performance index >= 58
    model.addConstr(12.5 * x0 + 19.96 * x1 + 12.75 * x2 >= 58, name="cognitive_performance_index_total")

    # 8. Total kidney support index >= 79
    model.addConstr(11.97 * x0 + 20.9 * x1 + 6.86 * x2 >= 79, name="kidney_support_index_total")

    # 9. -milligrams of zinc + milligrams of vitamin K >= 0
    model.addConstr(-x1 + x2 >= 0, name="zinc_vitamin_K_constraint")

    # 10. Total cognitive performance index of grams of carbohydrates and milligrams of zinc <= 132
    model.addConstr(12.5 * x0 + 19.96 * x1 <= 132, name="cognitive_performance_index_carbohydrates_zinc")

    # 11. Total cognitive performance index of milligrams of zinc and milligrams of vitamin K <= 145
    model.addConstr(19.96 * x1 + 12.75 * x2 <= 145, name="cognitive_performance_index_zinc_vitamin_K")

    # 12 & 13. Total cognitive performance index of all <= 90 and equals 90
    model.addConstr(12.5 * x0 + 19.96 * x1 + 12.75 * x2 <= 90, name="cognitive_performance_index_all")
    model.addConstr(12.5 * x0 + 19.96 * x1 + 12.75 * x2 == 90, name="cognitive_performance_index_all_exact")

    # 14. Total kidney support index of milligrams of zinc and milligrams of vitamin K <= 236
    model.addConstr(20.9 * x1 + 6.86 * x2 <= 236, name="kidney_support_index_zinc_vitamin_K")

    # 15. Total kidney support index of grams of carbohydrates and milligrams of zinc <= 251
    model.addConstr(11.97 * x0 + 20.9 * x1 <= 251, name="kidney_support_index_carbohydrates_zinc")

    # 16. Total kidney support index of all <= 251
    model.addConstr(11.97 * x0 + 20.9 * x1 + 6.86 * x2 <= 251, name="kidney_support_index_all")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Grams of carbohydrates: {x0.varValue}")
        print(f"Milligrams of zinc: {x1.varValue}")
        print(f"Milligrams of vitamin K: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```