## Symbolic Representation

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

- Let $x_1$ be the number of peanut butter smoothies and $x_2$ be the number of almond butter smoothies.

## Objective Function and Constraints

The objective function to maximize profit is: $5x_1 + 4x_2$

The constraints based on the available resources are:
- $3x_1 + 2x_2 \leq 50$ (almond milk constraint)
- $x_1 + 1.5x_2 \leq 40$ (protein powder constraint)
- $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints, as the number of smoothies cannot be negative)

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'peanut butter smoothies'), ('x2', 'almond butter smoothies')],
    'objective_function': '5*x1 + 4*x2',
    'constraints': [
        '3*x1 + 2*x2 <= 50',
        'x1 + 1.5*x2 <= 40',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("smoothie_problem")

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

# Objective function: maximize profit
model.setObjective(5*x1 + 4*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(3*x1 + 2*x2 <= 50, name="almond_milk_constraint")
model.addConstr(x1 + 1.5*x2 <= 40, name="protein_powder_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
    print(f"Maximum profit: ${5*x1.varValue + 4*x2.varValue:.2f}")
else:
    print("The model is infeasible.")
```