## Problem Description and Symbolic Representation

The problem can be broken down into the following components:

- **Variables:**
  - $x_1$ represents the number of bags of trail mix A to purchase.
  - $x_2$ represents the number of bags of trail mix B to purchase.

- **Objective Function:**
  The objective is to minimize the total cost. The cost of trail mix A is $5 per bag, and the cost of trail mix B is $8 per bag. Therefore, the objective function is $5x_1 + 8x_2$.

- **Constraints:**
  - The hiker needs at least 20 units of almonds. Since trail mix A contains 2 units of almonds per bag and trail mix B contains 2 units of almonds per bag, the constraint is $2x_1 + 2x_2 \geq 20$.
  - The hiker needs at least 15 units of chocolate chips. Since trail mix A contains 1 unit of chocolate chips per bag and trail mix B contains 3 units of chocolate chips per bag, the constraint is $x_1 + 3x_2 \geq 15$.
  - Additionally, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of bags cannot be negative.

## Symbolic Representation

```json
{
    'sym_variables': [('x1', 'bags of trail mix A'), ('x2', 'bags of trail mix B')],
    'objective_function': '5*x1 + 8*x2',
    'constraints': [
        '2*x1 + 2*x2 >= 20',
        'x1 + 3*x2 >= 15',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="trail_mix_A", lb=0, vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="trail_mix_B", lb=0, vtype=gurobi.GRB.INTEGER)

    # Objective function: minimize 5*x1 + 8*x2
    model.setObjective(5*x1 + 8*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(2*x1 + 2*x2 >= 20, name="almonds_constraint")
    model.addConstr(x1 + 3*x2 >= 15, name="chocolate_chips_constraint")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("The model is infeasible")

solve_trail_mix_problem()
```