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

**Decision Variables:**

* `x`: Number of regular rate tickets sold
* `y`: Number of concession rate tickets sold

**Objective Function:**

Maximize profit: `2x + 0.5y`

**Constraints:**

* **Capacity Constraint:** `x + y <= 120`
* **Minimum Concession Tickets:** `y >= 15`
* **Regular to Concession Ratio:** `x >= 5y`
* **Non-negativity:** `x >= 0`, `y >= 0`


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

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

# Create decision variables
x = model.addVar(lb=0, vtype=GRB.INTEGER, name="regular_tickets")
y = model.addVar(lb=0, vtype=GRB.INTEGER, name="concession_tickets")

# Set objective function
model.setObjective(2*x + 0.5*y, GRB.MAXIMIZE)

# Add constraints
model.addConstr(x + y <= 120, "capacity")
model.addConstr(y >= 15, "min_concession")
model.addConstr(x >= 5*y, "ratio")

# Optimize model
model.optimize()

# Print results
if model.status == GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Number of regular tickets: {x.x}")
    print(f"Number of concession tickets: {y.x}")
    print(f"Maximum profit: ${model.objVal}")
elif model.status == GRB.INFEASIBLE:
    print("Model is infeasible. No solution found.")
else:
    print(f"Optimization terminated with status: {model.status}")

```
