To solve Gabriel's problem, we need to formulate a linear programming model that minimizes his cost while meeting the daily nutritional requirements. Let's denote the pounds of lemons as $L$ and the pounds of pecans as $P$. The objective function to minimize is the total cost, which can be represented as $12L + 10P$. We have three constraints based on the nutritional requirements: calcium ($3L + 5P \geq 25$), potassium ($4L + 4P \geq 18$), and zinc ($7L + 9P \geq 19$). Additionally, since Gabriel cannot eat negative pounds of food, we have non-negativity constraints $L \geq 0$ and $P \geq 0$.

Here is the Gurobi code to solve this problem:

```python
from gurobipy import *

# Create a new model
m = Model("Gabriel_Nutrition")

# Add variables
lemons = m.addVar(name="lemons", lb=0)
pecans = m.addVar(name="pecans", lb=0)

# Set the objective function to minimize cost
m.setObjective(12*lemons + 10*pecans, GRB.MINIMIZE)

# Add constraints for nutritional requirements
m.addConstr(3*lemons + 5*pecans >= 25, name="calcium")
m.addConstr(4*lemons + 4*pecans >= 18, name="potassium")
m.addConstr(7*lemons + 9*pecans >= 19, name="zinc")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Gabriel should eat {lemons.x} pounds of lemons and {pecans.x} pounds of pecans.")
else:
    print("No optimal solution found. The problem might be infeasible.")

m.write("gabriel_nutrition.lp")
```