To solve this optimization problem, we first need to define the symbolic representation of the variables and the constraints as described in the problem statement.

The variables can be represented symbolically as follows:
- `x1` for 'grams of fat'
- `x2` for 'milligrams of calcium'

Given these variables, the objective function that needs to be maximized is:
\[ 2x_1 + 6x_2 \]

The constraints based on the problem description are:
1. The total combined cardiovascular support index from grams of fat and milligrams of calcium must be at least 42: 
   \[ 3x_1 + 10x_2 \geq 42 \]
2. The total combined energy stability index from grams of fat and milligrams of calcium should be at least 49:
   \[ 14x_1 + x_2 \geq 49 \]
3. \( 9x_1 - 8x_2 \geq 0 \)
4. The total combined cardiovascular support index from grams of fat plus milligrams of calcium should be no more than 88:
   \[ 3x_1 + 10x_2 \leq 88 \]
5. The total combined energy stability index from grams of fat plus milligrams of calcium should be no more than 62:
   \[ 14x_1 + x_2 \leq 62 \]

Thus, the symbolic representation of the problem in JSON format is:
```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'
    ]
}
```

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

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(name='grams_of_fat', lb=-GRB.INFINITY)  # No lower bound for x1, assuming it can be any real number
x2 = m.addVar(name='milligrams_of_calcium', lb=-GRB.INFINITY)  # No lower bound for x2

# Define the objective function: Maximize 2*x1 + 6*x2
m.setObjective(2*x1 + 6*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(3*x1 + 10*x2 >= 42, name='cardiovascular_support_index_min')
m.addConstr(14*x1 + x2 >= 49, name='energy_stability_index_min')
m.addConstr(9*x1 - 8*x2 >= 0, name='mixed_constraint')
m.addConstr(3*x1 + 10*x2 <= 88, name='cardiovascular_support_index_max')
m.addConstr(14*x1 + x2 <= 62, name='energy_stability_index_max')

# Optimize model
m.optimize()

if m.status == GRB.OPTIMAL:
    print(f"Optimal solution found: x1 = {x1.x}, x2 = {x2.x}")
else:
    print("No optimal solution found")
```