## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of iron', 'milligrams of vitamin B6', and 'grams of fat', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of iron'
- $x_2$ for 'milligrams of vitamin B6'
- $x_3$ for 'grams of fat'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $7.62x_1 + 3.72x_2 + 1.4x_3$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $x_1 \leq 229 / 15$ is not directly given, but we have $x_0 = 15$, $x_1 = 24$, $x_2 = 32$ as cognitive performance indices. The actual constraints from the problem statement are:
- $-7x_1 + 2x_3 \geq 0$
- $15x_1 + 32x_3 \leq 186$
- $24x_2 + 32x_3 \leq 218$
- $15x_1 + 24x_2 + 32x_3 \leq 218$

## 5: Determine the bounds for each variable based on the cognitive performance index
Given that the cognitive performance indices for $x_1$, $x_2$, and $x_3$ are 15, 24, and 32 respectively, and assuming these are upper bounds for their individual indices:
- No direct upper bounds are given for $x_1$, $x_2$, $x_3$ individually besides the cognitive performance index constraints.

## 6: Formulate the symbolic representation of the problem
The symbolic representation is:
- Variables: $x_1$ ('milligrams of iron'), $x_2$ ('milligrams of vitamin B6'), $x_3$ ('grams of fat')
- Objective function: Maximize $7.62x_1 + 3.72x_2 + 1.4x_3$
- Constraints:
  1. $-7x_1 + 2x_3 \geq 0$
  2. $15x_1 + 32x_3 \leq 186$
  3. $24x_2 + 32x_3 \leq 218$
  4. $15x_1 + 24x_2 + 32x_3 \leq 218$

## 7: Create the Gurobi code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="milligrams_of_iron", lb=0)  # No lower bound specified
    x2 = model.addVar(name="milligrams_of_vitamin_B6", lb=0)  # No lower bound specified
    x3 = model.addVar(name="grams_of_fat", lb=0)  # No lower bound specified

    # Define the objective function
    model.setObjective(7.62 * x1 + 3.72 * x2 + 1.4 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(-7 * x1 + 2 * x3 >= 0, name="constraint_1")
    model.addConstr(15 * x1 + 32 * x3 <= 186, name="constraint_2")
    model.addConstr(24 * x2 + 32 * x3 <= 218, name="constraint_3")
    model.addConstr(15 * x1 + 24 * x2 + 32 * x3 <= 218, name="constraint_4")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of iron: {x1.varValue}")
        print(f"Milligrams of vitamin B6: {x2.varValue}")
        print(f"Grams of fat: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 8: Provide the symbolic representation in JSON format
```json
{
    "sym_variables": [
        ["x1", "milligrams of iron"],
        ["x2", "milligrams of vitamin B6"],
        ["x3", "grams of fat"]
    ],
    "objective_function": "7.62x1 + 3.72x2 + 1.4x3",
    "constraints": [
        "-7x1 + 2x3 >= 0",
        "15x1 + 32x3 <= 186",
        "24x2 + 32x3 <= 218",
        "15x1 + 24x2 + 32x3 <= 218"
    ]
}
```