To solve this optimization problem, we first need to define the variables and the objective function symbolically, followed by the constraints. The variables are:

- `x1`: milligrams of iron
- `x2`: milligrams of vitamin B6
- `x3`: grams of fat

The objective function to maximize is: `7.62*x1 + 3.72*x2 + 1.4*x3`

The constraints, as described, are:
1. Cognitive performance index for each component does not directly translate into a constraint but rather informs us about the coefficients.
2. `-7*x1 + 2*x3 >= 0`
3. `15*x1 + 32*x3 <= 186`
4. `24*x2 + 32*x3 <= 218`
5. `15*x1 + 24*x2 + 32*x3 <= 218`

All variables are continuous, so we do not need to worry about integer constraints.

Given this information, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'milligrams of iron'), ('x2', 'milligrams of vitamin B6'), ('x3', 'grams of fat')],
    'objective_function': '7.62*x1 + 3.72*x2 + 1.4*x3',
    'constraints': [
        '-7*x1 + 2*x3 >= 0',
        '15*x1 + 32*x3 <= 186',
        '24*x2 + 32*x3 <= 218',
        '15*x1 + 24*x2 + 32*x3 <= 218'
    ]
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, name="milligrams_of_iron")
x2 = m.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, name="milligrams_of_vitamin_B6")
x3 = m.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, name="grams_of_fat")

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

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

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"milligrams_of_iron: {x1.x}")
    print(f"milligrams_of_vitamin_B6: {x2.x}")
    print(f"grams_of_fat: {x3.x}")
else:
    print("No optimal solution found.")
```