To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the given information.

### Symbolic Representation:

Let's define two variables:
- $x_1$ representing 'milligrams of vitamin B7'
- $x_2$ representing 'grams of fat'

The objective function to maximize is: $2(x_1)^2 + 3(x_1)$

The constraints based on the natural language description are:
1. Energy stability index for milligrams of vitamin B7: $32x_1$
2. Muscle growth index for milligrams of vitamin B7: $19x_1$
3. Energy stability index for grams of fat: $10x_2$
4. Muscle growth index for grams of fat: $32x_2$
5. Total combined energy stability index $\geq 21$: $32x_1 + 10x_2 \geq 21$
6. Total combined muscle growth index $\geq 70$: $19x_1 + 32x_2 \geq 70$
7. Constraint on vitamin B7 and fat: $7x_1 - 4x_2 \geq 0$
8. Maximum total combined energy stability index: $32x_1 + 10x_2 \leq 42$
9. Maximum total combined muscle growth index: $19x_1 + 32x_2 \leq 134$

Given the continuous nature of $x_1$ and $x_2$, we do not need to enforce integer solutions for either variable.

### Symbolic Representation in JSON Format:

```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B7'), ('x2', 'grams of fat')],
    'objective_function': '2*(x1)**2 + 3*x1',
    'constraints': [
        '32*x1 + 10*x2 >= 21',
        '19*x1 + 32*x2 >= 70',
        '7*x1 - 4*x2 >= 0',
        '32*x1 + 10*x2 <= 42',
        '19*x1 + 32*x2 <= 134'
    ]
}
```

### Gurobi Code:

To solve this optimization problem using Gurobi, we'll write the Python code as follows:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B7")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="grams_of_fat")

# Set the objective function
m.setObjective(2*x1**2 + 3*x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(32*x1 + 10*x2 >= 21, "total_energy_stability_index_min")
m.addConstr(19*x1 + 32*x2 >= 70, "total_muscle_growth_index_min")
m.addConstr(7*x1 - 4*x2 >= 0, "vitamin_B7_and_fat_constraint")
m.addConstr(32*x1 + 10*x2 <= 42, "total_energy_stability_index_max")
m.addConstr(19*x1 + 32*x2 <= 134, "total_muscle_growth_index_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B7: {x1.x}")
    print(f"Grams of Fat: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```