Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

*  `x`: Number of sleeper class tickets sold.
*  `y`: Number of regular tickets sold.

**Objective Function:**

Maximize profit: `500x + 200y`

**Constraints:**

* **Capacity Constraint:** `x + y <= 400`  (Total passengers cannot exceed 400)
* **Preference Constraint:** `y >= 5x` (At least 5 times more regular tickets than sleeper class)
* **Minimum Sleeper Class:** `x >= 50` (At least 50 sleeper class tickets)
* **Non-negativity:** `x >= 0`, `y >= 0` (Cannot sell negative tickets)


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

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

# Create variables
x = m.addVar(lb=0, vtype=GRB.INTEGER, name="sleeper_tickets") # Integer since you can't sell fractions of tickets
y = m.addVar(lb=0, vtype=GRB.INTEGER, name="regular_tickets")

# Set objective function
m.setObjective(500*x + 200*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x + y <= 400, "capacity")
m.addConstr(y >= 5*x, "preference")
m.addConstr(x >= 50, "min_sleeper")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal profit: ${m.objVal:.2f}")
    print(f"Sleeper class tickets: {x.x}")
    print(f"Regular tickets: {y.x}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
