## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin C' and 'milligrams of vitamin B12'. Let's denote 'milligrams of vitamin C' as $x_1$ and 'milligrams of vitamin B12' as $x_2$. The objective function to maximize is $8.21x_1 + 1.87x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
1. The muscle growth index of $x_1$ is 12.75.
2. $x_1$ has a digestive support index of 0.69.
3. $x_2$ has a muscle growth index of 7.81.
4. $x_2$ has a digestive support index of 1.13.
5. The total combined muscle growth index from $x_1$ and $x_2$ must be at least 47: $12.75x_1 + 7.81x_2 \geq 47$.
6. The total combined digestive support index from $x_1$ and $x_2$ must be greater than or equal to 22: $0.69x_1 + 1.13x_2 \geq 22$.
7. $6x_1 - 3x_2 \geq 0$.
8. The total combined muscle growth index from $x_1$ and $x_2$ must be at most 87: $12.75x_1 + 7.81x_2 \leq 87$.
9. The total combined digestive support index from $x_1$ and $x_2$ must be at most 52: $0.69x_1 + 1.13x_2 \leq 52$.

## 3: Create a symbolic representation of the problem
The symbolic representation is:
- Variables: $x_1$ for 'milligrams of vitamin C', $x_2$ for 'milligrams of vitamin B12'.
- Objective function: Maximize $8.21x_1 + 1.87x_2$.
- Constraints:
  1. $12.75x_1 + 7.81x_2 \geq 47$
  2. $0.69x_1 + 1.13x_2 \geq 22$
  3. $6x_1 - 3x_2 \geq 0$
  4. $12.75x_1 + 7.81x_2 \leq 87$
  5. $0.69x_1 + 1.13x_2 \leq 52$

## 4: Convert the problem into Gurobi code
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="milligrams_of_vitamin_C", lb=0)  # Assuming non-negative
x2 = model.addVar(name="milligrams_of_vitamin_B12", lb=0)  # Assuming non-negative

# Define the objective function
model.setObjective(8.21 * x1 + 1.87 * x2, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(12.75 * x1 + 7.81 * x2 >= 47)
model.addConstr(0.69 * x1 + 1.13 * x2 >= 22)
model.addConstr(6 * x1 - 3 * x2 >= 0)
model.addConstr(12.75 * x1 + 7.81 * x2 <= 87)
model.addConstr(0.69 * x1 + 1.13 * x2 <= 52)

# 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 B12: {x2.varValue}")
    print(f"Objective function value: {model.objVal}")
else:
    print("No optimal solution found.")
```

## 5: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin C'), ('x2', 'milligrams of vitamin B12')],
    'objective_function': '8.21*x1 + 1.87*x2',
    'constraints': [
        '12.75*x1 + 7.81*x2 >= 47',
        '0.69*x1 + 1.13*x2 >= 22',
        '6*x1 - 3*x2 >= 0',
        '12.75*x1 + 7.81*x2 <= 87',
        '0.69*x1 + 1.13*x2 <= 52'
    ]
}
```