## Step 1: Define the symbolic representation of the variables
The variables are 'grams of carbohydrates', 'grams of protein', and 'milligrams of potassium', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_1$ : grams of carbohydrates
- $x_2$ : grams of protein
- $x_3$ : milligrams of potassium

## 3: Formulate the objective function
The objective function to minimize is $2.87x_1 + 8.97x_2 + 2.78x_3$.

## 4: Define the constraints
The constraints are:
1. $3x_1 + 7x_2 \geq 10$
2. $3x_1 + 10x_3 \geq 17$
3. $3x_1 + 7x_2 + 10x_3 \geq 17$
4. $10x_2 - 5x_3 \geq 0$
5. $x_1 - 9x_2 \geq 0$

## 5: Consider the bounds of the muscle growth index
The muscle growth index for each variable is given, but the only explicit bound provided is an upper bound of 82 for the total muscle growth index, which is not directly assigned to a specific combination of variables but we have specific constraints.

## 6: Determine the Gurobi code
To solve this problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

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

# Define the variables
x1 = model.addVar(name="carbohydrates", lb=0)  # grams of carbohydrates
x2 = model.addVar(name="protein", lb=0)     # grams of protein
x3 = model.addVar(name="potassium", lb=0)   # milligrams of potassium

# Objective function
model.setObjective(2.87 * x1 + 8.97 * x2 + 2.78 * x3, gurobi.GRB.MINIMIZE)

# Constraints
model.addConstr(3 * x1 + 7 * x2 >= 10, name="carbohydrates_protein_mgi")
model.addConstr(3 * x1 + 10 * x3 >= 17, name="carbohydrates_potassium_mgi")
model.addConstr(3 * x1 + 7 * x2 + 10 * x3 >= 17, name="total_mgi")
model.addConstr(10 * x2 - 5 * x3 >= 0, name="protein_potassium_constraint")
model.addConstr(x1 - 9 * x2 >= 0, name="carbohydrates_protein_tradeoff")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Carbohydrates: {x1.varValue}")
    print(f"Protein: {x2.varValue}")
    print(f"Potassium: {x3.varValue}")
    print(f"Objective: {model.objVal}")
else:
    print("No optimal solution found.")
```

## 7: Symbolic Representation
The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'grams of carbohydrates'), ('x2', 'grams of protein'), ('x3', 'milligrams of potassium')],
    'objective_function': '2.87*x1 + 8.97*x2 + 2.78*x3',
    'constraints': [
        '3*x1 + 7*x2 >= 10',
        '3*x1 + 10*x3 >= 17',
        '3*x1 + 7*x2 + 10*x3 >= 17',
        '10*x2 - 5*x3 >= 0',
        'x1 - 9*x2 >= 0'
    ]
}
```