## Step 1: Define the symbolic representation of the variables
The variables are 'grams of carbohydrates', 'milligrams of zinc', and 'milligrams of vitamin K'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to maximize is $8x_1 + 3x_2 + 2x_3$.

## Step 3: List the constraints
The constraints given are:
- $12.5x_1 \leq 259$ (cognitive performance index for carbohydrates, but it seems there was a misunderstanding in directly translating the given data into constraints. The correct approach is to use the given attributes directly in the constraints)
- $11.97x_1 \leq 356$ (kidney support index for carbohydrates)
- $19.96x_2 \leq 259$ 
- $20.9x_2 \leq 356$
- $12.75x_3 \leq 259$
- $6.86x_3 \leq 356$
- $12.5x_1 + 19.96x_2 + 12.75x_3 \geq 58$ (total cognitive performance index)
- $11.97x_1 + 20.9x_2 + 6.86x_3 \geq 79$ (total kidney support index)
- $-x_2 + x_3 \geq 0$
- $12.5x_1 + 19.96x_2 \leq 132$
- $19.96x_2 + 12.75x_3 \leq 145$
- $12.5x_1 + 19.96x_2 + 12.75x_3 \leq 90$ (this seems to contradict the previous constraint of $\geq 58$ and $\leq 259$ for the total cognitive performance, indicating a potential error in problem transcription)
- $20.9x_2 + 6.86x_3 \leq 236$
- $11.97x_1 + 20.9x_2 \leq 251$
- $11.97x_1 + 20.9x_2 + 6.86x_3 \leq 251$

## 4: Correct and Refine Constraints Based on Given Resources/Attributes
Given resources/attributes:
- $r_0$: cognitive performance index with $x_0 = 12.5$, $x_1 = 19.96$, $x_2 = 12.75$
- $r_1$: kidney support index with $x_0 = 11.97$, $x_1 = 20.9$, $x_2 = 6.86$

## 5: Formulate the Optimization Problem
Maximize $8x_1 + 3x_2 + 2x_3$

Subject to:
- $12.5x_1 + 19.96x_2 + 12.75x_3 \geq 58$
- $11.97x_1 + 20.9x_2 + 6.86x_3 \geq 79$
- $-x_2 + x_3 \geq 0$
- $12.5x_1 + 19.96x_2 \leq 132$
- $19.96x_2 + 12.75x_3 \leq 145$
- $12.5x_1 + 19.96x_2 + 12.75x_3 \leq 90$
- $20.9x_2 + 6.86x_3 \leq 236$
- $11.97x_1 + 20.9x_2 \leq 251$
- $11.97x_1 + 20.9x_2 + 6.86x_3 \leq 251$

## 6: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'grams of carbohydrates'), ('x2', 'milligrams of zinc'), ('x3', 'milligrams of vitamin K')],
    'objective_function': '8*x1 + 3*x2 + 2*x3',
    'constraints': [
        '12.5*x1 + 19.96*x2 + 12.75*x3 >= 58',
        '11.97*x1 + 20.9*x2 + 6.86*x3 >= 79',
        '-x2 + x3 >= 0',
        '12.5*x1 + 19.96*x2 <= 132',
        '19.96*x2 + 12.75*x3 <= 145',
        '12.5*x1 + 19.96*x2 + 12.75*x3 <= 90',
        '20.9*x2 + 6.86*x3 <= 236',
        '11.97*x1 + 20.9*x2 <= 251',
        '11.97*x1 + 20.9*x2 + 6.86*x3 <= 251'
    ]
}
```

## 7: Gurobi Code
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(name="x1", lb=0)  # grams of carbohydrates
    x2 = model.addVar(name="x2", lb=0)  # milligrams of zinc
    x3 = model.addVar(name="x3", lb=0)  # milligrams of vitamin K

    # Objective function
    model.setObjective(8*x1 + 3*x2 + 2*x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(12.5*x1 + 19.96*x2 + 12.75*x3 >= 58)
    model.addConstr(11.97*x1 + 20.9*x2 + 6.86*x3 >= 79)
    model.addConstr(-x2 + x3 >= 0)
    model.addConstr(12.5*x1 + 19.96*x2 <= 132)
    model.addConstr(19.96*x2 + 12.75*x3 <= 145)
    model.addConstr(12.5*x1 + 19.96*x2 + 12.75*x3 <= 90)
    model.addConstr(20.9*x2 + 6.86*x3 <= 236)
    model.addConstr(11.97*x1 + 20.9*x2 <= 251)
    model.addConstr(11.97*x1 + 20.9*x2 + 6.86*x3 <= 251)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"x1 (grams of carbohydrates): {x1.varValue}")
        print(f"x2 (milligrams of zinc): {x2.varValue}")
        print(f"x3 (milligrams of vitamin K): {x3.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found")

solve_optimization_problem()
```