Here's the formulation and the Gurobi code:

**Decision Variables:**

* `x`: Number of train trips
* `y`: Number of truck trips

**Objective Function:**

Maximize the total number of packages transported:  `Maximize 80x + 50y`

**Constraints:**

* **Budget Constraint:** `50x + 40y <= 3000`
* **Train Trip Limit:** `x <= y`
* **Non-negativity:** `x >= 0`, `y >= 0`

```python
import gurobipy as gp
from gurobipy import GRB

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

# Create variables
x = m.addVar(vtype=GRB.INTEGER, name="train_trips")  # Integer since trips can't be fractional.
y = m.addVar(vtype=GRB.INTEGER, name="truck_trips")

# Set objective function
m.setObjective(80*x + 50*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(50*x + 40*y <= 3000, "budget_constraint")
m.addConstr(x <= y, "train_trip_limit")


# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Number of train trips (x): {x.x}")
    print(f"Number of truck trips (y): {y.x}")
    print(f"Total packages transported: {80*x.x + 50*y.x}")
elif m.status == GRB.INFEASIBLE:
    print("Model is infeasible.")
else:
    print(f"Optimization ended with status: {m.status}")

```
