## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin C', 'milligrams of vitamin B7', and 'grams of carbohydrates'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $8x_1 + 3x_2 + 9x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- $10x_1 + 23x_2 \leq 58$
- $10x_1 + 10x_3 \leq 99$
- $10x_1 + 23x_2 + 10x_3 \leq 99$
- $x_1, x_2, x_3 \geq 0$ (Implicit non-negativity constraints, though not explicitly stated, are common in such problems)

## 4: Specify the symbolic representation of the problem
The symbolic representation is:
- Variables: $(x_1, \text{milligrams of vitamin C}), (x_2, \text{milligrams of vitamin B7}), (x_3, \text{grams of carbohydrates})$
- Objective function: $8x_1 + 3x_2 + 9x_3$
- Constraints:
  1. $10x_1 + 23x_2 \leq 58$
  2. $10x_1 + 10x_3 \leq 99$
  3. $10x_1 + 23x_2 + 10x_3 \leq 99$

## 5: Implement the problem in Gurobi
To solve this linear programming problem using Gurobi, we will use Python.

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="milligrams_of_vitamin_C", lb=0)
    x2 = model.addVar(name="milligrams_of_vitamin_B7", lb=0)
    x3 = model.addVar(name="grams_of_carbohydrates", lb=0)

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

    # Add constraints
    model.addConstr(10*x1 + 23*x2 <= 58, name="vitamin_C_B7_constraint")
    model.addConstr(10*x1 + 10*x3 <= 99, name="vitamin_C_carbohydrates_constraint")
    model.addConstr(10*x1 + 23*x2 + 10*x3 <= 99, name="all_variables_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin C: {x1.varValue}")
        print(f"Milligrams of vitamin B7: {x2.varValue}")
        print(f"Grams of carbohydrates: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 6: Provide the symbolic representation in the required format
```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'
    ]
}
```