## Symbolic Representation

The problem can be converted into a symbolic representation as follows:

- **Variables:**
  - $x_1$ : Number of peanut butter smoothies
  - $x_2$ : Number of almond butter smoothies

- **Objective Function:**
  The profit per peanut butter smoothie is $3, and the profit per almond butter smoothie is $5. The objective is to maximize profit, so the objective function is:
  \[ \text{Maximize:} \quad 3x_1 + 5x_2 \]

- **Constraints:**
  - The shop has available 80 units of peanut butter, and one peanut butter smoothie requires 2 units of peanut butter. So, $2x_1 \leq 80$.
  - The shop has available 90 units of almond butter, and one almond butter smoothie requires 3 units of almond butter. So, $3x_2 \leq 90$.
  - The shop has available 100 units of milk, and one peanut butter smoothie requires 3 units of milk while one almond butter smoothie requires 3 units of milk. So, $3x_1 + 3x_2 \leq 100$.
  - Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$.

## JSON Representation

```json
{
    'sym_variables': [('x1', 'peanut butter smoothies'), ('x2', 'almond butter smoothies')],
    'objective_function': 'Maximize: 3*x1 + 5*x2',
    'constraints': [
        '2*x1 <= 80',
        '3*x2 <= 90',
        '3*x1 + 3*x2 <= 100',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="peanut_butter_smoothies", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="almond_butter_smoothies", lb=0, ub=gurobi.GRB.INFINITY)

    # Objective function: Maximize 3*x1 + 5*x2
    model.setObjective(3*x1 + 5*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(2*x1 <= 80, name="peanut_butter_constraint")
    model.addConstr(3*x2 <= 90, name="almond_butter_constraint")
    model.addConstr(3*x1 + 3*x2 <= 100, name="milk_constraint")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
        print(f"Maximum profit: {model.objVal}")
    else:
        print("No optimal solution found")

solve_smoothie_problem()
```