## Step 1: Define the symbolic representation of the variables
The variables are 'grams of fat' and 'milligrams of calcium', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $2x_1 + 6x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- The cardiovascular support index of grams of fat is 3: $3x_1 \leq 116$ but since it's an index, we consider it as a given attribute, not a constraint to be written.
- The energy stability index of grams of fat is 14: $14x_1 \leq 99$ but again, it's an attribute.
- Milligrams of calcium have a cardiovascular support index of 10: $10x_2 \leq 116$.
- The energy stability index of milligrams of calcium is 1: $1x_2 \leq 99$.
- The total combined cardiovascular support index from grams of fat and milligrams of calcium must be 42 at minimum: $3x_1 + 10x_2 \geq 42$.
- The total combined energy stability index from grams of fat and milligrams of calcium should be 49 at a minimum: $14x_1 + 1x_2 \geq 49$.
- $9x_1 - 8x_2 \geq 0$.
- The total combined cardiovascular support index from grams of fat plus milligrams of calcium should be 88 at a maximum: $3x_1 + 10x_2 \leq 88$.
- The total combined energy stability index from grams of fat plus milligrams of calcium should be 62 or less: $14x_1 + x_2 \leq 62$.

## 4: Correct and Compile the Constraints
Correcting and compiling the constraints:
1. $3x_1 + 10x_2 \geq 42$
2. $14x_1 + x_2 \geq 49$
3. $9x_1 - 8x_2 \geq 0$
4. $3x_1 + 10x_2 \leq 88$
5. $14x_1 + x_2 \leq 62$

## 5: Write down the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'grams of fat'), ('x2', 'milligrams of calcium')],
    'objective_function': '2*x1 + 6*x2',
    'constraints': [
        '3*x1 + 10*x2 >= 42',
        '14*x1 + x2 >= 49',
        '9*x1 - 8*x2 >= 0',
        '3*x1 + 10*x2 <= 88',
        '14*x1 + x2 <= 62'
    ]
}
```

## 6: Convert the problem into Gurobi code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="grams_of_fat", lb=0)  # Assuming non-negative
    x2 = model.addVar(name="milligrams_of_calcium", lb=0)  # Assuming non-negative

    # Define the objective function
    model.setObjective(2*x1 + 6*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3*x1 + 10*x2 >= 42)
    model.addConstr(14*x1 + x2 >= 49)
    model.addConstr(9*x1 - 8*x2 >= 0)
    model.addConstr(3*x1 + 10*x2 <= 88)
    model.addConstr(14*x1 + x2 <= 62)

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```