To solve the optimization problem described, we need to translate the given natural language description into a mathematical formulation that can be represented in Gurobi code. The objective is to maximize the function:

7.62 * (milligrams of iron) + 3.72 * (milligrams of vitamin B6) + 1.4 * (grams of fat)

Subject to several constraints:

1. Cognitive performance index for each component.
2. Linear inequalities involving the variables.
3. Upper bounds on combined cognitive performance indices.

Let's denote:
- \(x_0\) as milligrams of iron,
- \(x_1\) as milligrams of vitamin B6,
- \(x_2\) as grams of fat.

The constraints can be translated as follows:

1. Cognitive performance index constraints are given but seem to be more about the attributes of each variable rather than direct constraints on those variables.
2. \(-7x_0 + 2x_2 \geq 0\)
3. \(15x_0 + 32x_2 \leq 186\)
4. \(24x_1 + 32x_2 \leq 218\)
5. \(15x_0 + 24x_1 + 32x_2 \leq 218\)

Since the variables do not have to be integers, we can model this as a continuous linear programming problem.

Here's how you could implement this in Gurobi using Python:

```python
from gurobipy import *

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

# Define the variables
x0 = m.addVar(lb=0, name="milligrams_of_iron")
x1 = m.addVar(lb=0, name="milligrams_of_vitamin_B6")
x2 = m.addVar(lb=0, name="grams_of_fat")

# Set the objective function
m.setObjective(7.62*x0 + 3.72*x1 + 1.4*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(-7*x0 + 2*x2 >= 0, name="constraint_1")
m.addConstr(15*x0 + 32*x2 <= 186, name="constraint_2")
m.addConstr(24*x1 + 32*x2 <= 218, name="constraint_3")
m.addConstr(15*x0 + 24*x1 + 32*x2 <= 218, name="constraint_4")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print("milligrams_of_iron:", x0.x)
    print("milligrams_of_vitamin_B6:", x1.x)
    print("grams_of_fat:", x2.x)
else:
    print("No optimal solution found. Status:", m.status)

```

This code sets up the model according to the given problem, solves it using Gurobi's solver, and then prints out the values of the variables at the optimal solution if one is found.