To solve Gabriel's problem, we need to set up a linear programming model. Let's denote the number of acres used for growing pumpkins as \(P\) and the number of acres used for growing carrots as \(C\).

1. **Objective Function**: The objective is to maximize profit. Given that the profit per acre of pumpkin is $2.5 and the profit per acre of carrot is $3.4, we want to maximize \(2.5P + 3.4C\).

2. **Constraints**:
   - **Minimum acres for pumpkins**: \(P \geq 7\)
   - **Minimum acres for carrots**: \(C \geq 12\)
   - **Total available land**: \(P + C \leq 100\)
   - **Workforce limitation (carrots vs. pumpkins)**: \(C \leq 3P\)

Given these constraints and the objective function, we can now formulate this problem in Gurobi using Python.

```python
from gurobipy import *

# Create a model
m = Model("Gabriel_Farm")

# Define variables
P = m.addVar(vtype=GRB.CONTINUOUS, name="pumpkins", lb=0)
C = m.addVar(vtype=GRB.CONTINUOUS, name="carrots", lb=0)

# Set objective function to maximize profit
m.setObjective(2.5*P + 3.4*C, GRB.MAXIMIZE)

# Add constraints
m.addConstr(P >= 7, "min_pumpkins")
m.addConstr(C >= 12, "min_carrots")
m.addConstr(P + C <= 100, "total_land")
m.addConstr(C <= 3*P, "workforce_limitation")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Pumpkins: {P.x} acres")
    print(f"Carrots: {C.x} acres")
else:
    print("No optimal solution found")

```