To solve this optimization problem, we first need to define the decision variables and the objective function. Let's denote the number of bags of trail mix A as \(x_A\) and the number of bags of trail mix B as \(x_B\). The objective is to minimize the total cost, which can be represented as \(5x_A + 8x_B\).

The constraints are:
1. The hiker must eat at least 20 units of almonds. Since each bag of both trail mixes contains 2 units of almonds, this constraint can be written as \(2x_A + 2x_B \geq 20\).
2. The hiker must eat at least 15 units of chocolate chips. Given that trail mix A contains 1 unit and trail mix B contains 3 units of chocolate chips per bag, this constraint is represented as \(x_A + 3x_B \geq 15\).

Both \(x_A\) and \(x_B\) should be non-negative since the hiker cannot purchase a negative number of bags.

The problem can now be formulated in a standard form for linear programming problems:

- Minimize: \(5x_A + 8x_B\)
- Subject to:
  - \(2x_A + 2x_B \geq 20\)
  - \(x_A + 3x_B \geq 15\)
  - \(x_A, x_B \geq 0\)

This formulation directly translates into Gurobi code in Python. Here is how you can solve it:

```python
from gurobipy import *

# Create a new model
m = Model("Trail_Mix_Optimization")

# Add decision variables to the model
x_A = m.addVar(vtype=GRB.CONTINUOUS, name="trail_mix_A", lb=0)
x_B = m.addVar(vtype=GRB.CONTINUOUS, name="trail_mix_B", lb=0)

# Set the objective function: minimize total cost
m.setObjective(5*x_A + 8*x_B, GRB.MINIMIZE)

# Add constraints to the model
m.addConstr(2*x_A + 2*x_B >= 20, "almonds_constraint")
m.addConstr(x_A + 3*x_B >= 15, "chocolate_chips_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution found: Buy {x_A.x:.2f} bags of Trail Mix A and {x_B.x:.2f} bags of Trail Mix B.")
    print(f"Total cost: ${m.objVal:.2f}")
else:
    print("No optimal solution found. The problem might be infeasible.")
```