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

## Step 2: Define the objective function in symbolic notation
The objective function to maximize is $3.18x_1 + 3.26x_2$.

## Step 3: List the constraints in symbolic notation
The constraints given are:
- $10x_1 \leq 62$ (cognitive performance index for grams of fiber, but this seems to be an upper bound for the index, not directly a constraint on $x_1$)
- $13x_1 \leq 52$ (cardiovascular support index for grams of fiber)
- $16x_1 \leq 82$ (kidney support index for grams of fiber)
- $11x_2$ (cognitive performance index for milligrams of vitamin E, but again, this seems to be part of an index)
- $12x_2$ (cardiovascular support index for milligrams of vitamin E)
- $10x_2$ (kidney support index for milligrams of vitamin E)
- $10x_1 + 11x_2 \geq 13$ (total combined cognitive performance index)
- $13x_1 + 12x_2 \geq 18$ (total combined cardiovascular support index)
- $16x_1 + 10x_2 \geq 16$ (total combined kidney support index)
- $5x_1 - 10x_2 \geq 0$
- $10x_1 + 11x_2 \leq 61$ (total combined cognitive performance index upper bound)
- $13x_1 + 12x_2 \leq 50$ (total combined cardiovascular support index upper bound)
- $16x_1 + 10x_2 \leq 46$ (total combined kidney support index upper bound)
- $x_1$ is an integer
- $x_2$ is not restricted to integers

However, upon closer inspection, it seems the resource attributes given actually directly translate to:
- Cognitive performance index: $10x_1 + 11x_2 \geq 13$ and $10x_1 + 11x_2 \leq 61$
- Cardiovascular support index: $13x_1 + 12x_2 \geq 18$ and $13x_1 + 12x_2 \leq 50$
- Kidney support index: $16x_1 + 10x_2 \geq 16$ and $16x_1 + 10x_2 \leq 46$
And the direct constraints on $x_1$ and $x_2$ from their individual indexes are not as straightforward without the context of the indexes being directly constrained.

## 4: Correctly interpret the constraints
The actual constraints from the problem statement should be directly translated as:
- $10x_1 \leq 62$
- $13x_1 \leq 52$
- $16x_1 \leq 82$
Implying $x_1 \leq 6.15$, $x_1 \leq 4$, and $x_1 \leq 5.125$ respectively. Thus, $x_1 \leq 4$ is the most limiting.

## 5: Formulate the problem in Gurobi
Given the objective function and constraints, we can now formulate the problem.

## 6: Write the symbolic representation
```json
{
'sym_variables': [('x1', 'grams of fiber'), ('x2', 'milligrams of vitamin E')],
'objective_function': '3.18x1 + 3.26x2',
'constraints': [
    '10x1 <= 62',
    '13x1 <= 52',
    '16x1 <= 82',
    'x1 + 0x2 >= 0', 
    '0x1 + x2 >= 0',
    '10x1 + 11x2 >= 13',
    '13x1 + 12x2 >= 18',
    '16x1 + 10x2 >= 16',
    '5x1 - 10x2 >= 0',
    '10x1 + 11x2 <= 61',
    '13x1 + 12x2 <= 50',
    '16x1 + 10x2 <= 46',
    'x1 <= 4'
]
}
```

## 7: Implement the problem in Gurobi Python
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(name="grams_of_fiber", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="milligrams_of_vitamin_E")
    
    # Objective function
    model.setObjective(3.18*x1 + 3.26*x2, gurobi.GRB.MAXIMIZE)
    
    # Constraints
    model.addConstr(10*x1 <= 62)
    model.addConstr(13*x1 <= 52)
    model.addConstr(16*x1 <= 82)
    model.addConstr(10*x1 + 11*x2 >= 13)
    model.addConstr(13*x1 + 12*x2 >= 18)
    model.addConstr(16*x1 + 10*x2 >= 16)
    model.addConstr(5*x1 - 10*x2 >= 0)
    model.addConstr(10*x1 + 11*x2 <= 61)
    model.addConstr(13*x1 + 12*x2 <= 50)
    model.addConstr(16*x1 + 10*x2 <= 46)
    model.addConstr(x1 <= 4)
    
    # Optimize
    model.optimize()
    
    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Grams of fiber: {x1.varValue}")
        print(f"Milligrams of vitamin E: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```