To solve the optimization problem described, we need to break down the natural language description into a symbolic representation and then translate this into Gurobi code. The problem involves maximizing an objective function that combines milligrams of vitamin C, milligrams of vitamin B7, and grams of carbohydrates, subject to several constraints related to their muscle growth indices.

### Symbolic Representation

The variables in the problem can be represented symbolically as follows:
- Let \(x_1\) represent the quantity of milligrams of vitamin C.
- Let \(x_2\) represent the quantity of milligrams of vitamin B7.
- Let \(x_3\) represent the quantity of grams of carbohydrates.

The objective function to maximize is: \(8x_1 + 3x_2 + 9x_3\).

The constraints based on the muscle growth indices are:
1. \(10x_1 + 23x_2 \leq 58\)
2. \(10x_1 + 10x_3 \leq 99\)
3. \(10x_1 + 23x_2 + 10x_3 \leq 99\)

### Gurobi Code

To implement this optimization problem in Gurobi, we will use Python as the programming language.

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(name="vitamin_c", lb=0)  # Milligrams of vitamin C
x2 = m.addVar(name="vitamin_b7", lb=0)  # Milligrams of vitamin B7
x3 = m.addVar(name="carbohydrates", lb=0)  # Grams of carbohydrates

# Define the objective function
m.setObjective(8*x1 + 3*x2 + 9*x3, GRB.MAXIMIZE)

# Add constraints
m.addConstr(10*x1 + 23*x2 <= 58, name="constraint_1")
m.addConstr(10*x1 + 10*x3 <= 99, name="constraint_2")
m.addConstr(10*x1 + 23*x2 + 10*x3 <= 99, name="constraint_3")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Vitamin C: {x1.x}, Vitamin B7: {x2.x}, Carbohydrates: {x3.x}")
else:
    print("No optimal solution found.")

```

### JSON Representation of the Problem

```json
{
  "sym_variables": [
    ("x1", "milligrams of vitamin C"),
    ("x2", "milligrams of vitamin B7"),
    ("x3", "grams of carbohydrates")
  ],
  "objective_function": "8*x1 + 3*x2 + 9*x3",
  "constraints": [
    "10*x1 + 23*x2 <= 58",
    "10*x1 + 10*x3 <= 99",
    "10*x1 + 23*x2 + 10*x3 <= 99"
  ]
}
```