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

**Decision Variables:**

* `x`: Number of tacos sold
* `y`: Number of burritos sold

**Objective Function:**

Maximize profit: `3x + 6y`

**Constraints:**

* Minimum tacos: `x >= 50`
* Minimum burritos: `y >= 30`
* Maximum tacos: `x <= 80`
* Maximum burritos: `y <= 50`
* Maximum total items: `x + y <= 100`
* Non-negativity: `x, y >= 0` (implicitly handled by Gurobi for integer variables)


```python
import gurobipy as gp

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

# Create decision variables
x = model.addVar(vtype=gp.GRB.INTEGER, name="tacos")
y = model.addVar(vtype=gp.GRB.INTEGER, name="burritos")

# Set objective function
model.setObjective(3*x + 6*y, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(x >= 50, "min_tacos")
model.addConstr(y >= 30, "min_burritos")
model.addConstr(x <= 80, "max_tacos")
model.addConstr(y <= 50, "max_burritos")
model.addConstr(x + y <= 100, "max_total")

# Optimize the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Number of tacos to sell: {x.x}")
    print(f"Number of burritos to sell: {y.x}")
    print(f"Maximum profit: ${model.objVal}")
elif model.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status: {model.status}")

```
