## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B7' and 'grams of fat', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to maximize is $2x_1^2 + 3x_1$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $32x_1 + 10x_2 \geq 21$
- $19x_1 + 32x_2 \geq 70$
- $7x_1 - 4x_2 \geq 0$
- $32x_1 + 10x_2 \leq 42$
- $19x_1 + 32x_2 \leq 134$
- $x_1$ and $x_2$ can be non-negative and non-whole numbers.

## 4: Correct and Simplify Constraints
Upon review, there seems to be a repetition in the constraints. The correct and simplified set should consider:
- The energy stability index constraint: $32x_1 + 10x_2 \geq 21$ and $32x_1 + 10x_2 \leq 42$
- The muscle growth index constraint: $19x_1 + 32x_2 \geq 70$ and $19x_1 + 32x_2 \leq 134$
- The interaction constraint: $7x_1 - 4x_2 \geq 0$

## 5: Symbolic Representation
The symbolic representation of the problem is:
```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'
]
}
```

## 6: Gurobi Code
Now, let's implement this in Gurobi:
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="milligrams_of_vitamin_B7", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="grams_of_fat", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

    # Objective function
    model.setObjective(2*x1**2 + 3*x1, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(32*x1 + 10*x2 >= 21, name="energy_stability_index_min")
    model.addConstr(19*x1 + 32*x2 >= 70, name="muscle_growth_index_min")
    model.addConstr(7*x1 - 4*x2 >= 0, name="interaction_constraint")
    model.addConstr(32*x1 + 10*x2 <= 42, name="energy_stability_index_max")
    model.addConstr(19*x1 + 32*x2 <= 134, name="muscle_growth_index_max")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B7: {x1.varValue}")
        print(f"Grams of fat: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```