## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of potassium', 'milligrams of vitamin E', 'grams of carbohydrates'], which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : milligrams of potassium
- $x_1$ : milligrams of vitamin E
- $x_2$ : grams of carbohydrates

## 3: Define the objective function in symbolic notation
The objective function to minimize is $7x_0 + 4x_1 + 1x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $9x_0 \leq 121$
- $14x_0 \leq 169$
- $3x_1 \leq 121$
- $15x_1 \leq 169$
- $5x_2 \leq 121$
- $13x_2 \leq 169$
- $9x_0 + 3x_1 \geq 40$
- $3x_1 + 5x_2 \geq 27$
- $9x_0 + 5x_2 \geq 25$
- $9x_0 + 3x_1 + 5x_2 \geq 25$
- $15x_1 + 13x_2 \geq 50$
- $14x_0 + 15x_1 \geq 51$
- $14x_0 + 15x_1 + 13x_2 \geq 50$
- $10x_0 - 7x_1 \geq 0$
- $-3x_1 + 9x_2 \geq 0$
- $9x_0 + 3x_1 \leq 65$
- $9x_0 + 5x_2 \leq 73$
- $9x_0 + 3x_1 + 5x_2 \leq 55$
- $14x_0 + 15x_1 + 13x_2 \leq 87$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'milligrams of potassium'), ('x1', 'milligrams of vitamin E'), ('x2', 'grams of carbohydrates')],
    'objective_function': '7*x0 + 4*x1 + 1*x2',
    'constraints': [
        '9*x0 <= 121',
        '14*x0 <= 169',
        '3*x1 <= 121',
        '15*x1 <= 169',
        '5*x2 <= 121',
        '13*x2 <= 169',
        '9*x0 + 3*x1 >= 40',
        '3*x1 + 5*x2 >= 27',
        '9*x0 + 5*x2 >= 25',
        '9*x0 + 3*x1 + 5*x2 >= 25',
        '15*x1 + 13*x2 >= 50',
        '14*x0 + 15*x1 >= 51',
        '14*x0 + 15*x1 + 13*x2 >= 50',
        '10*x0 - 7*x1 >= 0',
        '-3*x1 + 9*x2 >= 0',
        '9*x0 + 3*x1 <= 65',
        '9*x0 + 5*x2 <= 73',
        '9*x0 + 3*x1 + 5*x2 <= 55',
        '14*x0 + 15*x1 + 13*x2 <= 87'
    ]
}
```

## Step 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="x0", lb=0)  # milligrams of potassium
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin E
    x2 = model.addVar(name="x2", lb=0)  # grams of carbohydrates

    # Define the objective function
    model.setObjective(7 * x0 + 4 * x1 + x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(9 * x0 <= 121)
    model.addConstr(14 * x0 <= 169)
    model.addConstr(3 * x1 <= 121)
    model.addConstr(15 * x1 <= 169)
    model.addConstr(5 * x2 <= 121)
    model.addConstr(13 * x2 <= 169)
    model.addConstr(9 * x0 + 3 * x1 >= 40)
    model.addConstr(3 * x1 + 5 * x2 >= 27)
    model.addConstr(9 * x0 + 5 * x2 >= 25)
    model.addConstr(9 * x0 + 3 * x1 + 5 * x2 >= 25)
    model.addConstr(15 * x1 + 13 * x2 >= 50)
    model.addConstr(14 * x0 + 15 * x1 >= 51)
    model.addConstr(14 * x0 + 15 * x1 + 13 * x2 >= 50)
    model.addConstr(10 * x0 - 7 * x1 >= 0)
    model.addConstr(-3 * x1 + 9 * x2 >= 0)
    model.addConstr(9 * x0 + 3 * x1 <= 65)
    model.addConstr(9 * x0 + 5 * x2 <= 73)
    model.addConstr(9 * x0 + 3 * x1 + 5 * x2 <= 55)
    model.addConstr(14 * x0 + 15 * x1 + 13 * x2 <= 87)

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```